Jieshuang Jia

Alessandro Furlan

Sara Gonzalez-Hilarion

Catherine Leroy

Dieter C Gruenert

David Tulasne

Fabrice Lejeune
email: fabrice.lejeune@inserm.fr

Caspases shutdown nonsense-mediated mRNA decay during apoptosis

Keywords: decay during apoptosis NMD, apoptosis, caspase, mRNA quality control, UPF protein, cell death

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Cell death is a natural process that occurs throughout development and the life of a multicellular organism removing cells that are no longer needed or have become pathogenic, thereby organizing tissues and participating in their homeostasis. There are many pathways leading to cell death that are activated by numerous external and/or internal stimuli [START_REF] Elmore | Apoptosis: a review of programmed cell death[END_REF]. Cell death can occur via programmed processes such as apoptosis, autophagy, necrosis (a more passive process) or necroptosis (a type of programmed necrosis) [START_REF] Galluzzi | Molecular definitions of cell death subroutines: recommendations of the Nomenclature Committee on Cell Death 2012[END_REF].

During apoptosis, specific gene networks and protein-cleavage programs are activated sending the cells on a death spiral [START_REF] Reimertz | Gene expression during ER stressinduced apoptosis in neurons: induction of the BH3-only protein Bbc3/PUMA and activation of the mitochondrial apoptosis pathway[END_REF][START_REF] Zhang | Induction of acetylcholinesterase expression during apoptosis in various cell types[END_REF][START_REF] Johnson | Endothelial cells preparing to die by apoptosis initiate a program of transcriptome and glycome regulation[END_REF] through a family of cysteine-aspartate proteases (caspases) [START_REF] Alnemri | Human ICE/CED-3 protease nomenclature[END_REF]. Caspases are classified by their role in the apoptotic pathway, into (i) initiator caspases (such as caspases2, 8, 9 and 10) or (ii) effector caspases (such as caspases3, 6 or 7) [START_REF] Frejlich | Caspases and their role in gastric cancer[END_REF]. Initiator caspases cleave the inactive precursor of the effector caspases (pro-caspases) into their active forms. Effector caspases are then responsible for cleaving protein targets to interfere with cellular processes, and in particular, with the activation of some endonucleases that degrade genomic DNA.

Throughout the process of mRNA maturation, numerous quality control mechanisms verify the integrity of the information carried by mRNAs. One of these, nonsense-mediated mRNA decay (NMD), leads to the rapid decay of mRNAs harboring a premature termination codon (PTC) to prevent the synthesis of non-functional and/or potentially detrimental truncated proteins [START_REF] Popp | The dharma of nonsense-mediated mRNA decay in Mammalian cells[END_REF][START_REF] Muhlemann | Recognition and elimination of nonsense mRNA[END_REF][START_REF] Karam | Regulation of nonsense-mediated mRNA decay: implications for physiology and disease[END_REF]. In addition to its role in quality control, NMD also regulates gene expression of so-called natural substrates of NMD [START_REF] Mendell | Nonsense surveillance regulates expression of diverse classes of mammalian transcripts and mutes genomic noise[END_REF][START_REF] Viegas | The abundance of RNPS1, a protein component of the exon junction complex, can determine the variability in efficiency of the Nonsense Mediated Decay pathway[END_REF][START_REF] Sureau | SC35 autoregulates its expression by promoting splicing events that destabilize its mRNAs[END_REF][START_REF] Wollerton | Autoregulation of polypyrimidine tract binding protein by alternative splicing leading to nonsense-mediated decay[END_REF][START_REF] Lareau | Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements[END_REF]. Proteins that play a central role in NMD, such as UPF1, UPF2, UPF3/UPF3a, and UPF3X/UPF3b are highly conserved from yeast to human. The requirement for these UPF proteins in NMD is illustrated by the fact that the down-regulation of any one of them results in an inhibition of NMD [START_REF] Mendell | Separable roles for rent1/hUpf1 in altered splicing and decay of nonsense transcripts[END_REF][START_REF] Chan | A UPF3-mediated regulatory switch that maintains RNA surveillance[END_REF]. NMD is a necessary component in the development and maintenance of healthy cells and organisms. For example, UPF1 is an essential gene since inactivation of UPF1 protein leads to early embryonic death in mouse [START_REF] Medghalchi | Rent1, a transeffector of nonsense-mediated mRNA decay, is essential for mammalian embryonic viability[END_REF], thus implicating NMD as a critical proof-reading and/or regulatory component in early organismal development. However, the question of whether NMD is required after cells have committed to proceed along a pathway that culminates in cell death has not been investigated. To address this question, NMD efficiency was studied during apoptosis. The studies presented here show that NMD factors UPF1 and UPF2 are cleaved by caspases3 and 7 during apoptosis. The functional consequences of these cleavages are a general shutdown of NMD activity leading to stabilization of both PTC-containing mRNAs and natural substrates of NMD, and also the production of caspase-cleaved UPF fragments that induce apoptosis and inhibit NMD.

Results

UPF proteins are cleaved during apoptosis

Since apoptosis effectors such as caspases target various proteins [START_REF] Shi | Caspase activation, inhibition, and reactivation: a mechanistic view[END_REF], studies were carried out to investigate the possibility that components of the NMD system could themselves be targets. Putative caspase cleavage sites in UPF proteins were identified using the Support Vector Machine technology for predicting caspase substrate cleavage sites [START_REF] Wee | SVM-based prediction of caspase substrate cleavage sites[END_REF]. Several putative caspase cleavage sites are present in human UPF proteins (supplementary Figure S1). This suggests that these proteins could be targeted by caspases during apoptosis.

To determine if UPF proteins are targeted for degradation during apoptosis, the detection of the fulllength proteins and putative UPF cleavage fragments was assessed in apoptotic cells. HeLa cells, incubated with increasing amounts of staurosporine [START_REF] Koh | Staurosporine-induced neuronal apoptosis[END_REF][START_REF] Zwelling | N-(5,5-diacetoxypentyl)doxorubicin: a novel anthracycline producing DNA interstrand cross-linking and rapid endonucleolytic cleavage in human leukemia cells[END_REF], were assayed for the rate of apoptosis after 24hrs by quantifying the incorporation of annexin V and propidium iodide into the cells (Figure 1A). This shows that treatment with 2 or 4µM staurosporine results in about 50% apoptotic cells. At higher staurosporine concentrations, killing was much quicker and more extensive and therefore unsuitable for further NMD efficiency analysis (data not shown). Subsequent studies used 2µM staurosporine as a working concentration combined with a 24hrs exposure.

Next, caspase activation was evaluated using caspase cleavage of PARP or caspase3 (Figure 1B) and the level of UPF proteins was assessed (Figure 1C). UPF1 and UPF2 protein levels were lower in cells treated with staurosporine compared to those exposed to DMSO. An about 110kDa UPF1 cleavage fragment was detected in staurosporine-treated extracts using an anti-UPF1 antibody, raised against the C-terminus of UPF1, that could correspond to a C-terminal fragment generated upon cleavage at the putative caspase cleavage sites EFTD 37 or DEED 111 (supplemental Figure S1).

Western-blot analysis of UPF2 protein with an antibody recognizing the amino terminal residues identified a new UPF2 isoform of about 75kDa in staurosporine-treated cells (Figure 1C). This UPF2 isoform could correspond to a N-terminal fragment generated by a cleavage either after DLID 585 or MHLD 741 (supplemental Figure S1). UPF3X level was not affected and additional fragments were not detected by staurosporine treatment even though there are at least two putative canonical caspase cleavage sites present in this protein (Figure 1C and supplemental Figure S1). Overall, the results show that staurosporineinduced apoptosis reduced the level of UPF1 and UPF2 proteins suggesting a possible inhibition of NMD during apoptosis. Specifically, the putative UPF1 caspase cleavage site would separate the Nterminal conserved region of UPF1, shown to be essential for the function of UPF1 in NMD [START_REF] Kashima | Binding of a novel SMG-1-Upf1-eRF1-eRF3 complex (SURF) to the exon junction complex triggers Upf1 phosphorylation and nonsense-mediated mRNA decay[END_REF], from the rest of the protein (Figure 2). Little is known about the role of UPF2 in NMD; however, a recent study has shown that the first and/or the second MIF4G domains of UPF2 are essential for NMD [START_REF] Clerici | Structural and functional analysis of the three MIF4G domains of nonsense-mediated decay factor UPF2[END_REF].

Given that the UPF2 caspase cleavage site is predicted to be located in the second MIF4G making UPF2 nonfunctional during apoptosis (Figure 2).

To identify of the UPF protein species detected by Western-blot in the presence of staurosporine, UPF1 or UPF2 were targeted with siRNAs and almost abolished UPF1 or UPF2 related bands, respectively, but not the non-specific bands (supplemental Figure S2A).

To evaluate if UPF1 and UPF2 protein cleavage was restricted to staurosporine, other apoptosis activators were assessed (supplemental Figure S2B). These studies showed that anisomycin, etoposid, AA2 and cisplatin also promote cleavage of UPF1 and UPF2, indicating that the effect of staurosporine is a generalized feature of apoptosis inducers (supplemental Figures S2B andS5).

A caspase inhibitor prevents UPF factors cleavage during apoptosis

To investigate the possible role of caspases in the cleavage of NMD factors UPF1 and UPF2 during apoptosis, HeLa cells were incubated with staurosporine in the presence or in the absence of zVADfmk, a pan-caspase inhibitor. As expected, staurosporine-induced apoptosis and caspase activation were reversed in the presence of the zVAD-fmk (Figure 3B).

To demonstrate that caspases are responsible for the cleavage of NMD factors UPF1 and UPF2 after staurosporine treatment, the levels of UPF1 and UPF2 proteins in cells incubated with or without zVAD-fmk were analyzed by western-blotting (Figure 3C). As expected (Figure 1), staurosporine treatment decreased the levels of UPF1 and UPF2 and showed the appearance of UPF1 and UPF2 cleavage products (Figure 3C). In contrast, in the presence of staurosporine and zVAD-fmk, the levels of UPF1 and UPF2 were almost identical to those in the presence of the DMSO, and no cleavage products were detected (Figure 3C). Overall, these results indicate that caspases are responsible for the cleavage of UPF1 and UPF2 during apoptosis.

Caspase3 and caspase7 can cleave UPF1 and UPF2

To identify the caspases responsible for UPF1 and UPF2 cleavage, HeLa-cell extracts were incubated with purified caspases3, 6, 7, 8 or 9. The results indicate that UPF1 and UPF2 levels were reduced in the presence of caspase3 (Figure 4). Consistent with fragments generated during apoptosis (Figures 1 and3), a shorter isoform of UPF1 of about 110kDa, and the cleavage product of UPF2 of about 75kDa, were also detected after treatment of the HeLa-cell extract with caspase3.

Although the amount of full-length UPF1 and UPF2 proteins decreased in the presence of caspase7, no cleavage fragments were detected. This suggests that UPF cleavage fragments generated by caspase7 are unstable. Nevertheless, the results presented in Figure 4 indicate that UPF1 and UPF2 are targeted by caspase3 and/or caspase7 suggesting that NMD can be inhibited during apoptosis.

NMD is inhibited during staurosporine-or AA2-induced apoptosis

To characterize the functional consequences of UPF1 and UPF2 cleavage during apoptosis, the efficiency of NMD was first measured in HeLa cells. Cells were transfected with -globin gene expression vectors encoding mRNA that was either wild-type (Norm) or harbored a nonsense mutation at codon 39 (Ter) which is an NMD target [START_REF] Zhang | Intron function in the nonsense-mediated decay of betaglobin mRNA: indications that pre-mRNA splicing in the nucleus can influence mRNA translation in the cytoplasm[END_REF][START_REF] Thermann | Binary specification of nonsense codons by splicing and cytoplasmic translation[END_REF]. After a 24hrs exposure to 2µM staurosporine or DMSO, the level of globin Ter mRNA in the cells was about 3 times higher in the presence of staurosporine than in the presence of DMSO (Figure 5A). These results indicate that NMD is inhibited during apoptosis induced by staurosporine.

The efficiency of NMD during apoptosis was also evaluated in cell lines that produce an endogenous mutated mRNA targeted for NMD. Immortalized cystic fibrosis airway epithelial cells, IB3 harboring the W1282X nonsense mutation in CFTR gene or, alternatively, 6CFSMEo-harboring the Q2X nonsense mutation in CFTR gene (Supplemental Figure S3) [START_REF] Cozens | Characterization of immortal cystic fibrosis tracheobronchial gland epithelial cells[END_REF][START_REF] Da Paula | Characterization of novel airway submucosal gland cell models for cystic fibrosis studies[END_REF] were also assayed. We first determined that 2M staurosporine was the lowest concentration that provided efficient apoptosis in each cell line (Supplemental Figure S3A) even though this level of apoptosis is about 2 fold less than that obtained in HeLa cells. Under these conditions, CFTR mRNA levels were about 2.5 to 4 fold higher in the presence of staurosporine than in the presence of DMSO (Supplemental Figure S3B). Inhibition of NMD in the presence of staurosporine was even greater than that observed in the presence of amlexanox, a recently identified NMD inhibitor [START_REF] Gonzalez-Hilarion | Rescue of nonsense mutations by amlexanox in human cells[END_REF]. To evaluate whether the staurosporine effect observed on PTC-containing mRNAs was not the result of a general increase in transcription, the level of CFTR pre-mRNA was assayed and shown not to significantly change in the presence of staurosporine (Supplemental Figure S4). Overall, these results demonstrate that NMD can be inhibited in several cell-types during staurosporine-induced apoptosis. Due to the higher level of apoptosis measured in HeLa cells when compared to that in IB3 and 6CFSMEo-cells, further experiments were performed in HeLa cells.

Since NMD is inhibited during apoptosis, studies were undertaken to determine whether natural substrates of NMD were also stabilized during apoptosis. The mRNA levels of three different natural NMD substrates (SC35, NAT9 and TBL2) [START_REF] Mendell | Nonsense surveillance regulates expression of diverse classes of mammalian transcripts and mutes genomic noise[END_REF][START_REF] Viegas | The abundance of RNPS1, a protein component of the exon junction complex, can determine the variability in efficiency of the Nonsense Mediated Decay pathway[END_REF][START_REF] Sureau | SC35 autoregulates its expression by promoting splicing events that destabilize its mRNAs[END_REF] were measured in cells incubated with staurosporine or DMSO. The mRNA levels of these substrates were compared to the level of three reference mRNAs that are not subject to NMD (GAPDH, Actin or RPL32). The level of the three natural substrates of NMD showed a two to four fold of increase compared to the level of the three reference mRNAs (Figure 5B). This result shows that the NMD regulation occurring on natural substrates of NMD is also inhibited during apoptosis.

To demonstrate the involvement of caspases in the inhibition of NMD during apoptosis, NMD efficiency was assessed in the presence of zVAD-fmk. The Figure 6 shows that NMD inhibition under staurosporine treatment was also reversed in the presence of zVAD-fmk. It is noteworthy that the presence of the zVAD-fmk alone had no effect on NMD efficiency when apoptosis is not activated, likely because caspases are not in play (Figure 6).

To exclude the possibility that UPF1 and UPF2 cleavage, and NMD inhibition observed during apoptosis is an indirect effect related to staurosporine exposure, NMD was evaluated after exposure to the AA2 apoptosis inducer [START_REF] Nguyen | Direct activation of the apoptosis machinery as a mechanism to target cancer cells[END_REF]. AA2 activates caspases in a cytochrome c dependent manner to potentiate apoptosis. After a 16hrs exposure to 50M AA2 more than 20% of the HeLa cells went through apoptotic cell death (supplemental Figure S5A). Under these conditions, the level of exogenous Globin Ter mRNA was about 4 fold higher than in vehicle-treated cells (supplemental Figure S5B). AA2 exposure also caused a decrease of the full-length UPF1 and UPF2 proteins, and the appearance of caspase cleavage products, thereby demonstrating that different inducers of apoptosis can also inhibit NMD (supplemental Figure S5C).

Caspase cleavage fragments from UPF1 or UPF2 are inducers of apoptosis and/or NMD inhibitors

The presence of cleaved UPF1 or UPF2 fragments during apoptosis in cells treated with staurosporine or AA2 raises the question of their role in NMD inhibition and/or in apoptosis. The specific identity of the caspase cleavage sites in the UPF1 and UPF2 proteins was determined by mutating aspartic acid to asparagine at positions 37 or 111 in UPF1 and 585 or 741 in UPF2 to block caspase cleavage. CFPtagged constructs were used in order to discriminate full-length proteins and their cleavage products from their endogenous counterparts. Overexpression of the wild-type or D111N UPF1 transgene, showed a baseline presence of the UPF1 cleavage fragment that increased with staurosporine. No cleavage fragment was detected with the D37N UPF1 construct (Figure 7A). With regard to UPF2, the caspase cleavage product was not detected under any conditions using the CFP-UPF2 constructs for reasons that remain unclear. A decrease in CFP-UPF2 protein was observed with the wild-type or the D585N UPF2 construct, while no decrease was observed with the D741N UPF2 construct (Figure 7B).

These results indicate that the caspase cleavage site is located at position 37 in UPF1 and at position 741 in UPF2 (Figure 7).

To evaluate the impact of N-or C-terminal UPF1 or UPF2 fragments generated by caspase cleavage on apoptosis, HeLa cells were transfected with an expression vector encoding a CFP-fusion peptide with the N-terminal part of UPF1 (CFP UPF1-Nter), C-terminal part of UPF1 (CFP UPF1-Cter), N-terminal part of UPF2 (CFP UPF2-Nter) or the C-terminal part of UPF2 (CFP UPF2-Cter) corresponding to each UPF1 or UPF2 caspase-cleaved fragments. The expression level of each exogenous caspase cleaved UPF protein fragments was assessed by western-blotting analysis 48 hours after transfection (Supplemental Figure S6). At the expression levels of exogenous UFP1 or UFP2 fragments indicated there was about 10-fold increase in apoptosis in cells expressing the N-terminal caspase-cleavage fragment from UPF1 or UPF2, while cells expressing the C-terminal fragments or the full-length UPF1 or UPF2 proteins showed no such increase (figure 8A). These results suggest that the N-terminal caspase-cleaved fragment from both UPF proteins promote apoptosis.

To determine the contribution of UPF caspase cleavage fragments to the rate of staurosporineinduced apoptosis, UPF1 or UPF2 were down-regulated with siRNAs (supplemental Figure S7).

Although, UPF1 or UPF2 caspase cleavage fragments were not detectable in the presence of UPF1 or UPF2 siRNAs, respectively, the rate of staurosporine-induced apoptosis remains unchanged even though the down-regulation by itself of UPF1 or UPF2 by siRNAs induces apoptosis (supplemental Figure S7B), suggesting that the production of UPF1 and UPF2 apoptotic cleavage fragments play only a modest role in cell death.

To assess the effect of the caspase UPF1 and UPF2 cleaved fragments on NMD, HeLa cells were transfected with vectors encoding Globin Norm or Ter, MUP and one of the caspase-generated N-or C-terminal fragments, or the full-length UPF1 or UPF2 (Figure 8B). Assessment of NMD showed about 2-3 folds NMD inhibition in cells expressing either the C-terminal fragment of UPF1 or the N-terminal fragment of UPF2. The dominant negative interference of the C-terminal fragment of UPF1 on NMD is consistent with a previous report [START_REF] Kashima | Binding of a novel SMG-1-Upf1-eRF1-eRF3 complex (SURF) to the exon junction complex triggers Upf1 phosphorylation and nonsense-mediated mRNA decay[END_REF]. Interestingly, the N-terminal UPF2 fragment shows the capacity of both inducing apoptosis and inhibiting NMD.

Assessment of NMD inhibition during apoptosis in vivo

Our previous results indicate that NMD is inhibited as a consequence of activating the cell-intrinsic apoptotic pathway. To generalize these results we interrogated whether the activation of the cellextrinsic (death receptor) apoptotic pathway is also able to inhibit NMD and whether the shutdown of NMD can occur in vivo. Mice were injected with an agonistic anti-Fas antibody to ligate the Fas death receptor and initiate cell-extrinsic apoptosis in hepatocytes as previously described [START_REF] Lefebvre | Caspasegenerated fragment of the Met receptor favors apoptosis via the intrinsic pathway independently of its tyrosine kinase activity[END_REF]. The impact of hepatic apoptosis on NMD was assessed through the analysis of liver mRNAs and proteins after anti-Fas antibody exposure. The apoptotic status of hepatocytes was monitored by the presence of cleaved caspase3 (Figure 9A). The in silico analysis of mouse UPF sequences shows that the caspase cleavage sites identified for UPF1 and UPF2 are conserved between mice and human (supplemental Figure S1). In apoptotic livers, a decrease was observed in the level of UPF1 protein, without a corresponding decrease in UPF3X (Figure 9A). UPF1 cleavage products were not detected, suggesting that they may be more unstable in vivo than in vitro or that the apoptosis was too massive and the UPF cleavage fragments are already degraded at the time of the cell harvesting. Analysis of UPF2 levels was not possible, since none of the UPF2 antibodies used could detect mouse UPF2 protein (data not shown).

NMD efficiency in mice was measured by exploiting the conservation of the alternative splicing between human and mouse at intron 3 of the mouse Srsf7 gene [START_REF] Lareau | Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements[END_REF][START_REF] Lejeune | Alternative splicing of intron 3 of the serine/arginine-rich protein 9G8 gene. Identification of flanking exonic splicing enhancers and involvement of 9G8 as a trans-acting factor[END_REF]. The retention of intron 3 in Srsf7 mRNA introduces PTCs recognized by NMD [START_REF] Lareau | Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements[END_REF][START_REF] Lejeune | Alternative splicing of intron 3 of the serine/arginine-rich protein 9G8 gene. Identification of flanking exonic splicing enhancers and involvement of 9G8 as a trans-acting factor[END_REF]. Quantification of the alternative Srsf7 transcripts in mice exposed to anti-Fas antibody showed a 5-fold increase in intron 3-containing mRNA transcript compared to mice injected with saline (Figure 9B). General inhibition of splicing during apoptosis can be excluded since retention of intron 4 in Srsf7 mRNA was not detected in the PCR amplification product encompassing intron 3 to exon 5 used in this analysis (Figure 9B). Overall, the results in Figure 9 indicate that NMD is inhibited during apoptosis both in vitro and in vivo.

Discussion

During apoptosis, caspases target a wide panel of proteins to promote cell death. In silico prediction programs reveal putative caspase cleavage sites in UPF proteins encoding key NMD factors (Supplemental Figure S1), and we experimentally demonstrate that UPF1 and UPF2 but not UPF3X are cleaved during staurosporine-induced apoptosis (Figure 1). These cleavages are prevented when the pan-caspase inhibitor zVAD-fmk is added in the presence of staurosporine, indicating that caspases are responsible for the cleavage of UPF1 and UPF2 during apoptosis (Figure 3). In vitro results implicate caspase3 at least in mediating UPF1 and UPF2 cleavage (Figure 4). Due to the central role of UPF proteins in NMD, the functional consequence of the cleavage of NMD factors UPF1 and UPF2 is the inhibition of NMD, as demonstrated for transfected as well as for endogenous nonsense mutation-containing genes and natural substrates of NMD in different cell lines (Figure 5 and Supplemental Figure S3). Moreover, staurosporine-induced NMD inhibition was reversed with the pan-caspase inhibitor, zVAD-fmk (Figure 6). These findings are further reinforced by the use of AA2, which confirmed the cleavage of UPF1 and UPF2 (Supplemental Figure S5). Together, these results indicate that both staurosporine and AA2 represent two new non-specific NMD inhibitors.

More importantly, this study demonstrates that the inhibition of NMD observed during apoptosis in cultured cells also extends to tissues undergoing apoptosis in vivo (Figure 9). It was particularly noteworthy that UPF1 and UPF2 were targeted by caspases, while UPF3X was not, despite the presence of putative caspase cleavage sites in UPF3X (supplemental Figure S1). Targeting UPF1 to efficiently inactivate NMD is reasonable since all NMD pathways are unified by their requirement for UPF1, unlike UPF2 [START_REF] Gehring | Exon-junction complex components specify distinct routes of nonsense-mediated mRNA decay with differential cofactor requirements[END_REF]. The targeting of UPF2 by caspases suggests that either UPF2 plays a more central role in NMD than previously thought or other functions of UPF2 unrelated to its role in NMD must be inhibited for apoptotic cell death to progress. It is possible that there are other NMD-related proteins, such as SMG proteins or EJC components targeted by caspases to ensure the complete arrest of NMD. For example, proteins from the translation initiation complex have been shown to be targeted by caspases during apoptosis to block more than 60% of the protein synthesis [START_REF] Clemens | Translation initiation factor modifications and the regulation of protein synthesis in apoptotic cells[END_REF]. Among these proteins, eIF4G which plays a central role in the pioneer round of translation through its interaction with PABPC1 and CBP80, is targeted and inactivated by caspase3 [START_REF] Marissen | Eukaryotic translation initiation factor 4G is targeted for proteolytic cleavage by caspase 3 during inhibition of translation in apoptotic cells[END_REF].

The work presented here shows that UPF1 and UPF2 cleavage occurs at positions D37 and D741, respectively (Figure 7) not only to inactivate both NMD factors per se but also to generate fragments that induce apoptosis (Figure 8A). However, this contribution to apoptosis appears to be modest and is likely due to the wide spectrum of caspase targets that influence apoptosis [START_REF] Reimertz | Gene expression during ER stressinduced apoptosis in neurons: induction of the BH3-only protein Bbc3/PUMA and activation of the mitochondrial apoptosis pathway[END_REF][START_REF] Zhang | Induction of acetylcholinesterase expression during apoptosis in various cell types[END_REF][START_REF] Johnson | Endothelial cells preparing to die by apoptosis initiate a program of transcriptome and glycome regulation[END_REF]. Further investigations will be necessary to elucidate the mechanism underlying the induction of apoptosis by these UPF cleavage fragments, to identify the proteins that bind these fragments and in particular the highly conserved N-terminal UPF1 fragment for which there is, as yet, no clear function. With respect to UPF2, specific functions have not been correlated with the different regions of UPF2 other than the interaction domain in the C-terminal region with UPF1 and SMG1 [START_REF] Clerici | Structural and functional analysis of the three MIF4G domains of nonsense-mediated decay factor UPF2[END_REF]. This C-terminal region partially retains the UPF2 function in NMD and is consistent with findings showing a dominant negative effect of the N-terminal caspase-cleavage UPF2 fragment on NMD, which is not observed with the C-terminal caspase-cleavage fragment (Figure 8). Our study shows that the N-terminal part of UPF2 has the dual property to induce apoptosis and to inhibit NMD making this region of interest to understand the function of the full-length UPF2.

During apoptosis, various pathways that ultimately lead to cell death are either inhibited or activated. NMD no longer appears to be required during apoptosis, given that the level of PTCcontaining mRNAs increase in the presence of apoptosis activators (Figures 5, 6 and9 and supplemental Figures S3 andS5). The levels of natural substrates of NMD are also upregulated during apoptosis (Figure 5B and Figure 9B) which could promote apoptosis by allowing the expression of toxic proteins.

In this study, the results show a bidirectional link between NMD and apoptosis in the sense that apoptosis induces NMD inhibition and caspase-cleaved UPF fragments promote apoptosis. The induction of apoptosis by NMD inhibition has previously been demonstrated during embryogenesis in particular. Apoptosis, as well as NMD, is essential during embryogenesis, and is involved in removing unnecessary cells to help shape and define tissues [START_REF] Suzanne | Shaping organisms with apoptosis[END_REF] and organs. NMD is likely involved in degrading inaccurate mRNAs that could interfere with the normal developmental program. In the event of a molecular dysfunction, such as NMD arrest during embryogenesis, apoptosis and eventually embryonic lethality can occur. For instance, the absence of UPF1 in 3.5 days post coitum (dpc) results in blastocysts death by apoptosis [START_REF] Medghalchi | Rent1, a transeffector of nonsense-mediated mRNA decay, is essential for mammalian embryonic viability[END_REF], while the lack of SMG1 leads to embryonic death at day 8.5dpc by apoptosis [START_REF] Mcilwain | Smg1 is required for embryogenesis and regulates diverse genes via alternative splicing coupled to nonsensemediated mRNA decay[END_REF]. However, NMD inhibition in vivo does not always lead to activation of apoptosis. This is illustrated by the UPF2 knock-out mice that do not undergo apoptotic embryonic death [START_REF] Thoren | UPF2 is a critical regulator of liver development, function and regeneration[END_REF]. Other examples include NMD inhibition by amlexanox where there is no effect on cell viability [START_REF] Gonzalez-Hilarion | Rescue of nonsense mutations by amlexanox in human cells[END_REF], or more recently, by a new set of NMD inhibitors targeting the interaction between UPF1 and SMG7 proteins with a low cellular toxicity [START_REF] Martin | Identification and characterization of small molecules that inhibit nonsense mediated RNA decay and suppress nonsense p53 mutations[END_REF]. Whether NMD inhibition can activate apoptosis might be dependent on the degree of NMD inhibition.

The data presented here demonstrate that apoptosis regulates NMD in addition to other already identified molecular pathways such as miRNAs, Staufen-mediated mRNA decay or the response to stress (for review see [START_REF] Karam | Regulation of nonsense-mediated mRNA decay: implications for physiology and disease[END_REF]). The data also raise the question of whether there are other cell death pathways that promote NMD inhibition. For example, apoptotic-like programmed cell death does not involve caspases activation, but rather an apoptosis-inducing factor (AIF) [START_REF] Susin | Bcl-2 inhibits the mitochondrial release of an apoptogenic protease[END_REF]. Recent studies have also suggested that inhibition of NMD can lead to autophagy (41) ; however, whether NMD is inhibited during distinct autophagic inductions has yet to be determined. Cell death by necrosis is yet another mechanism that involves a family of proteases, the calpains rather than caspases. It would therefore be of interest to assess whether NMD is also active during these other cell death pathways.

Material and methods

Cell Culture

HeLa cells were incubated in DMEM supplemented with 10% foetal bovine serum (FBS) and ZellShield (Minerva Biolabs). IB3 cells were grown in LHC-8 supplemented with 10% FBS and ZellShield, and 6CFSMEo- [START_REF] Cozens | Characterization of immortal cystic fibrosis tracheobronchial gland epithelial cells[END_REF][START_REF] Da Paula | Characterization of novel airway submucosal gland cell models for cystic fibrosis studies[END_REF][START_REF] Gruenert | Established cell lines used in cystic fibrosis research[END_REF] and 16HBE14o- [START_REF] Gruenert | Established cell lines used in cystic fibrosis research[END_REF][START_REF] Cozens | CFTR expression and chloride secretion in polarized immortal human bronchial epithelial cells[END_REF] were grown in -MEM supplemented with 10% FBS, 1mM L-glutamine and ZellShield. Chemical treatment of the cells involved: Staurosporine (Sigma-Aldrich) -at 2 µM for 24 hrs; Z-VAD-FMK (Santa Cruz Biotechnology) -20 M added 30 min before incubating the cells with staurosporine; amlexanox (Sequoia Research) -25 M for 24hrs; apoptosis activator 2 (Santa Cruz Biotechnology) -50 M for 16 hrs; Anisomycin (Santa Cruz Biotechnology) at 50 μM, Etoposide (Sigma) at 50μM, or Cisplatin (Mylan) at 50μg/ml for 24hrs.

qRT-PCR analysis

qRT-PCR analysis was performed as previously described [START_REF] Gonzalez-Hilarion | Rescue of nonsense mutations by amlexanox in human cells[END_REF][START_REF] Durand | Inhibition of nonsense-mediated mRNA decay (NMD) by a new chemical molecule reveals the dynamic of NMD factors in P-bodies[END_REF]. The list of primer sequences used in this study is: Globin (sense: 5'GGACGAGCTGTACAAGTATC3', antisense:5'GGGTTTAGTGGTACTTGTGAGC3'), MUP (sense: 5'CTGATGGGGCTCTATG3', antisense:

5'TCCTGGTGAGAAGTCTCC3'), CFTR mRNA (sense: 5'GGCCAGAGGGTGGGCCTCTT3', antisense:

5'AGGAAACTGTTCTATCACAG3'), CFTR pre-mRNA (sense: 5'TTGATGCCTAGAGGGCAGAT3', antisense 5'TGTCAAAGGGATTGGGAGGG3'), SC35 (sense: 5'CCTCTTAAGAAAATGCTGCGGTCTC3',antisense: 5'ATCAGCCAAATCAGTTAAAATCTGC3'), NAT9 (sense: 5'ATTGTGCTGGATGCCGAGA3', antisense: 5'ACCTAGCGTGGTCACTCCGTA3'), TBL2 (sense: 5'GCAGTCATTTACCACATGC3', antisense:

5'TATTGTTTCTGCTTCTTGGAT3'), GAPDH (sense: 5'CATTGACCTCAACTACATGG3', antisense: 5'GCCATGCCAGTGAGCTTCC3'), Actin (sense: 5'ATGAGGTAGTCAGTCAGGTCCC3', antisense: 5'CAGAAGGATTCCTATGTG GGCG3'), RPL32(sense:5'TTGACAACAGGGTTCGTAG3',antisense:5'TTCTTGGAG GAAACATTGTG3'), mouse SRSF7

(sense (intron 3):5'CTTCGTTTGAGTCAGTCGCC3', antisense (exon 5): 5'AAGCTGATCTTGATCTACG3').

Western blot analysis

Proteins were extracted in lysis buffer (50mM Tris pH7, 20mM EDTA and 5% SDS) and then loaded on a 10% SDS-PAGE (except UPF1 which was loaded on 6% SDS-PAGE). After migration, proteins were transferred to a nitrocellulose membrane. The membrane was incubated with a primary antibody overnight at 4°C and then with a secondary antibody coupled to HRP activity (Jackson Immuno Research) at 4°C for 2hrs. Finally, proteins were detected using SuperSignal West Femto Maximum Sensitivity Substrate (Pierce). Primary antibodies used were: rabbit anti-hUPF1 at 1/5000 (Abcam), rabbit anti-hUPF2 at 1/5000 (antibody raised against the following synthetic peptide: H2N -MPA ERK KPA SME EKD C -CONH2 (Eurogentec)), rabbit anti-UPF3X at 1/5000 (Aviva Systems Biology), rabbit anti-PARP at 1/1000 (Santa Cruz Biotechnology), rabbit cleaved Caspase-3 (Asp175) at 1/1000 (Cell Signaling Technology), rabbit anti-tubulin at 1/1000 (Epitomics), mouse anti-eIF4E at 1/200 (Santa Cruz Biotechnology) and rabbit anti-GFP at 1/5000 (Sigma).

Apoptosis assays

Apoptosis was measured according to the manufacturer's protocol (Life Technologies) using the Tali Apoptosis Kit -containing annexin V Alexa Fluor 488 and propridium iodide. Measurements were performed on a Tali cytometer (Life Technologies). Apoptotic cells were identified as the cells incorporating only annexin V.

In vivo apoptosis assay

Mouse experiments were performed according to institutional guidelines. Briefly, 6-week old male C57BL/6 mice, fasted overnight and were then intraperitoneally injected with the anti-Fas Jo2 antibody (BD Pharmingen) at 0.2µg/g body weight or saline buffer vehicle as a control. After 4 hrs, mice were euthanized and livers were harvested.

Caspase cleavage reaction

Assessment of caspase cleavage was as previously described [START_REF] Foveau | Amplification of apoptosis through sequential caspase cleavage of the MET tyrosine kinase receptor[END_REF]. Briefly, 3 x 10 5 HeLa cells/well were distributed in 6 well-plates. After 24hrs, cells were lysed in 80 l of the lysis buffer (20mM Pipes pH7.2, 100 mM NaCl, 1% Chaps, 10% Sucrose + 5 mMDTT+50 M EDTA). Cell extracts were then incubated with 1µl of purified caspase for 4hrs at 37°C before loading on a 10% SDS-PAGE for western-blot analysis. Purified, active caspases were generously provided by Dr GS Salvesen (The Burnham institute, La Jolla, CA, USA). The concentration of purified caspase 3 is 13.9 M, 7.5 µM for caspase 6, 19.7 M for caspase 7, 15 M for caspase 8 and 41.5 M for caspase 9.

Construction of the plasmids expressing UPF fragments

UPF1 derived plasmids: CFP-UPF1 cDNA was introduced into pUNO vector (Cayla) linearized by BamHI using the Infusion cloning kit (Clontech). CFP-UPF1 was amplified by PCR with primer for CFP: sense: 5'AGATCACCGGCGTGTCGACGATGGTGAGCAAGGGCGAGGA3'; antisense: 5'CGTACGCCTCCACGCTCATCTTGTACAGCTCGTCCATGCC3'; primers for UPF1: sense: 5'ATGAGCGTGGAGGCGTACG3'; antisense: 5'TGGCTGCAGAGCGCTGGATCTTAATACTGGGACAGCC3').

The same method was used to amplify CFP-UPF1Nter (amino-acid 1 to 37) using the same primer sense as for CFP-UPF1 and the antisense primer:

5'TGGCTGCAGAGCGCTGGATCTTAGTCGGTGAACTCGAACTCGG3'), and CFP-UPF1Cter (amino-acid 38 to 1118) was amplified using the sense primer: 5'TTTACTCTTCCTAGCCAGACG3' and the same antisense primer as for CFP-UPF1.

Construction of mutant UPF plasmids

Aspartic acid was changed to asparagine using QuikChange Site-Directed Mutagenesis Kit Authors declare that they have no conflict of interest.

UPF2 derived plasmids were

  constructed according to Gateway cloning technology (Life Technologies) using empty vector pSPO-CFP-rfc. The list of primers used to amplify UPF2 full length or fragments is: for the full length the full-length UPF2 (called as CFP UPF2 expressing amino fragment of UPF2 (called as CFP UPF2-Nter expressing amino acids from 1 to 741) (sense: 5' GGGGACA AGTTTGTACAAAAAAGCAGGCTCCATGC CAGCTGAGCGTAAAAAGCCAG', antisense: 5' GGGG ACCACTTTGTACAAGAA AGCTGG GT ATCAAGATGC ATTGCTTGCTTCTTT 3'), the C-terminal fragment of UPF2 (called as CFP UPF2-Cter expressing amino acids from 742 to 1272) (sense:

  short interfering RNA (siRNA): HeLa cells were transfected with the ICAFectin TM 442 reagent (In Cell Art) complexed with either 100 nM of siControl (Eurogentec), or with siUPF1 (5'-AAGATGCAGTTCCGCTCCATTTT-3') or siUPF2 (5'-GAAGTTGGTACGGGCACTC-3'). At 24h post-transfection, cells were incubated with DMSO or 2µM of Staurosporine for an additional 24h. The cells were then collected to measure apoptosis by AnnexinV or were extracted in lysis buffer for western blot analysis. mouse facility of Institut Pasteur de Lille. JJ is funded by Vaincre la mucoviscidose. FL received fundings for this project from Vaincre la mucoviscidose, l'Association Française contre les myopathies and la Fondation ARC. DCG was supported by funds from Pennsylvania Cystic Fibrosis, Inc and Cystic Fibrosis Research, Inc.

Figure 1 :

 1 Figure 1: NMD factors UPF1 and UPF2 are cleaved during apoptosis. (A) Determination of the working

Figure 2 :

 2 Figure 2: Schematic representation of UPF1 and UPF2 domains. Functional and interacting domains

Figure 3 :

 3 Figure 3: Caspases are responsible for the cleavage of NMD factors UPF1 and UPF2 during apoptosis.

Figure 4 :

 4 Figure 4: Identification of caspases responsible for the UPF1 or UPF2 cleavage during apoptosis. HeLa

Figure 5 :

 5 Figure 5: NMD is inhibited during apoptosis. (A) Measure of NMD efficiency as determined by

Figure 6 :

 6 Figure 6: Caspases are responsible for the NMD inhibition during apoptosis. HeLa cells were

Figure 7 :

 7 Figure 7: Identification of the caspase cleavage sites in UPF1 and UPF2. (A) Identification of the

Figure 8 :

 8 Figure 8: UPF caspase-cleavage fragments induce apoptosis and/or NMD inhibition. (A) Measure of

Figure 9 :

 9 Figure 9: NMD inhibition during apoptosis in vivo. Mice injected with saline buffer or with anti-Fas

Acknowledgments

Authors would like to thank Dr. Jérome Vicogne for helpful discussions and Pr. Lynne Maquat, Dr Edouard Bertrand and Dr. GS Salvesen for reagents and comments on the paper. We also thank the

Error bar=SD, the bracket indicate that the student t-test did not show any significant differences between these codnitions. Results are representative of 2 independent experiments.


Kamal Danouchi

Guillaume Prenat

Senior, IEEE Lorena Anghel

Spin Orbit Torque-based Crossbar Array for Error Resilient Binary Convolutional Neural Network

Convolutional Neural Network (CNN) is one of the most important Deep Neural Networks (DNN) classes that helps solving many tasks related to image recognition and computer vision. Their classical implementations by using conventional CMOS technologies and digital design techniques are still considered very energy-consuming. Floating point CNN relies primarily on MAC (Multiply and ACcumulate) operation.

Recently, cost-effective Bite-wise CNN based on XNOR and bit-counting operations have been considered as a possible hardware implementation candidate. However, the Von-Neumann bottleneck due to intensive data fetching between memory and the computing core limits their scalability on hardware. XNOR-BITCOUNT operations can be easily implemented by using In Memory Computing (IMC) paradigms executed on a memristive crossbar array. Among emerging memristive devices, the Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) offers the possibility to have a higher ON resistance that allows reducing the reading current, since all the crossbar array is read in parallel. This could contribute to a further reduction of energy consumption, paving the way for much bigger crossbar designs. This study presents a crossbar architecture based on SOT-MRAM with very low energy consumption; we study the impact of process variability on the synaptic weights and perform Monte-Carlo simulations of the overall crossbar array to evaluate the error rate. Simulation results show that this implementation has lower energy consumption with respect to other memristive solutions with 65.89 fJ per read operation. The design is also quite robust to process variations, with very low reading inaccuracies up to 10 %.

I. INTRODUCTION

The deluge of data generated by the digital world has seen the emergence of tools to extrapolate useful information out of it, such as Deep Neural Network (DNN). Nowadays these networks complete several human tasks, such as facial recognition, voice recognition, or language processing [START_REF] Lecun | Convolutional networks for images, speech, and time series[END_REF] [2] to name just a small number of them. However, the hardware implementation of these networks still raises many questions about energy consumption, mostly due to the Von Neumann bottleneck [START_REF] Yu | Neuro-inspired computing with emerging nonvolatile memorys[END_REF], the continuous movements between the processing core and the memory being the main cause of energy over-consumption for Artificial Neural Network (ANN). To tackle this issue, one of the solutions is to perform the calculation inside the memory (In Memory Computing -IMC) avoiding data fetching between the processing unit and the memory. This paradigm relies on the use of crossbar arrays,

The authors are with the Univ. Grenoble Alpes, CEA, CNRS, Grenoble INP, IRIG-Spintec, 38000 Grenoble, France (e-mail: kamal.danouchi@cea.fr) that performs the computation of NN operations in an analog fashion and in one cycle. This architecture stores the weights in a Non-Volatile memory (NVM) based on memristive devices. Inputs will be applied in parallel under the form of voltages to the crossbar matrix then weighted by the resistive synaptic devices and summed along the column thanks to Kirchhoff's laws. However, encoding weights on synaptic devices is not straightforward as we are limited by the number of discernible conductance states of the synaptic devices.

In the past few years, many studies have proposed quantization methods to avoid using floating point parameters which are energy and memory intensive such as Ternary Neural Network (TNN) or Binary Neural Network (BNN) [START_REF] Zhu | Trained Ternary Quantization[END_REF] [START_REF] Li | Ternary Weight Networks[END_REF] [6] [START_REF] Courbariaux | Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1[END_REF]. Binary networks could be seen as the lowest level of precision or quantification, weights and activation have only binary values (+1 [START_REF] Lecun | Convolutional networks for images, speech, and time series[END_REF] and -1[0]). This feature simplifies the weight mapping, reduces memory access and the overall energy consumption.

On the other hand, memristive devices used to store synaptic weights are subjected to non-idealities due to process variation, temperature, and parasitic effects [START_REF] Chakraborty | Resistive Crossbars as Approximate Hardware Building Blocks for Machine Learning: Opportunities and Challenges[END_REF]. This, coupled with the loss of information due to the binarization can result in a loss of accuracy in hardware implementation. In the present work, a binary crossbar architecture is implemented using Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) devices in which we evaluate the errors that can induce accuracy loss in the network. Then, we study the variability impact on the output of the crossbar through intensive simulations. We show the high energy efficiency of the SOT-MRAM crossbar array and its robustness against process variations.

II. PRELIMINARY A. Related Work

Our research focuses on the implementation of BNNs on hardware for IMC accelerators. In this scope, there is a significant number of studies on the implementation of these networks, particularly on Resistive Random Access Memory (RRAM) devices with the conventional 2T-2R bit-cell. In [START_REF] Bocquet | In-memory and error-immune differential rram implementation of binarized deep neural networks[END_REF] [10], the authors emphasize the reliability of such structures against errors. For a crossbar implementation, the most prominent study is XNOR-RRAM [START_REF] Sun | Xnor-rram: A scalable and parallel resistive synaptic architecture for binary neural networks[END_REF], where the authors implemented XNOR and POPCOUNT operations in parallel as for conventional DNN implementation. The study in [START_REF] Giacomin | A robust digital rram-based convolutional block for low-power image processing and learning applications[END_REF] presents a 4T2R bitcell robust against device variations affecting the inference operations. In spintronics technology, [START_REF] Jung | A crossbar array of magnetoresistive memory devices for in-memory computing[END_REF] unveiled a BNN implementation where the authors replaced the traditional summation of currents in a crossbar with a summation of resistances thus reducing the energy consumption of Spin-Transfer-Torque Magnetic Random-access Memory (STT-MRAM). Nonetheless, the main limit of this approach is the number of devices that could be implemented in the array due to analog noise. In addition, the use of time-to-digital converters in their design induces a higher latency. In this paper, we present a 4T-2R SOT-MRAM crossbar implementation for BNN inference. The SOT-MRAM separates the read and write operation, therefore the write operation is not limited by the Magnetic Tunnel Junction (MTJ) resistance level and its resistance can be tunned up to dozen of MΩ. The SOT can therefore be implemented in a traditional crossbar scheme. Moreover, the SOT-MRAM offers also two stable states and it is less affected by drift and retention issues than RRAM and Phase Change Memory (PCM) devices [START_REF] Wang | Resistive switching materials for information processing[END_REF].

B. Convolutional Neural Network

Convolutional Neural Network (CNN) is a bio-inspired Artificial Neural Network (ANN), mainly used for image recognition [START_REF] Alzubaidi | Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions[END_REF]. It consists of convolution and max pooling layers used to extract feature maps and a Fully-Connected (FC) layer used for classification. The objective of the network is to classify with a higher or lower probability which image is fed into the network using Multi-Perceptron Layers (MLP). Since convolution is the most computation-intensive operation in CNN, several studies have proposed a bitwise CNN and specific memory access aware strategies. [START_REF] Simons | A review of binarized neural networks[END_REF], [START_REF] Rastegari | Xnor-net: Imagenet classification using binary convolutional neural networks[END_REF].

C. Binary Neural Networks

Due to their extreme compression of the operands used in the computation, BNNs have been considered for implementation on embedded devices [START_REF] Yuan | A comprehensive review of binary neural network[END_REF]. Unlike classical neural networks, BNN encodes the weights as well as the neuron activation values in binary format, rather than floating point formats. To allow this, during training the real values of the variables are compared to a threshold, accordingly to that the network will generate either a +1 [START_REF] Lecun | Convolutional networks for images, speech, and time series[END_REF] or -1[0] as can be seen below:

sign(x) = +1 x > 0 -1 x < 0 (1)
Here x represents a floating point value calculated during training, after passing through the sign function, x is binarized. Unlike the Multiplication and ACcumulation (MAC) operations performed by conventional networks, binary networks perform a logical XNOR operation followed by a pop-count operation. This facilitates its implementation on the hardware since these operations are not very energy consuming. As in most DNNs today, a batch normalization (BN) layer is added to stabilize and accelerate the training phase [START_REF] Ioffe | Batch normalization: Accelerating deep network training by reducing internal covariate shift[END_REF]. This layer is also simply implemented by a threshold operation:

τ = µ β - β * σ 2 β + ε γ (2)

D. Spin-orbit torque magnetic random access memory

In recent years, many resistive synaptic devices have been proposed to implement DNN [START_REF] Wang | Resistive switching materials for information processing[END_REF], such as PCM, RRAM or STT-MRAM. STT-MRAM devices offer advantages in terms of writing speed (few ns), endurance (≥ 10 12 cycles), and low power consumption (around 10f J) [START_REF] Wang | Resistive switching materials for information processing[END_REF], [START_REF] Dieny | Opportunities and challenges for spintronics in the microelectronics industry[END_REF]. Thus for memory applications, a lot of companies have invested in STT-MRAM with already products on the shelves [21] [22]. Notwithstanding its strong performance for memory implementation, STT-MRAM is not a suitable candidate for conventional crossbar implementation due to its low resistance level (i.e. few kΩ). Indeed, to perform Matrix-Vector Multiplication (MVM) in the array, all the bitcells are simultaneously read in parallel for an analog computation. As a result, this low resistance level will lead to a significant power draw in the array. To overcome this concern, SOT-MRAM is proposed [START_REF] Doevenspeck | SOT-MRAM Based Analog in-Memory Computing for DNN Inference[END_REF]. According to the authors, the low resistance state of this memory could be tuned up to 100 MΩ. The SOT-MRAM is a three-terminal device that consists of an MTJ mounted on a heavy metal substrate, with the MTJ being the main core of all MRAM devices. This nanostructure is composed of two ferromagnetic layers separated by a tunnel barrier, with one of the layers being the Free Layer (FL) and the other one the Reference Layer (RL). The resistance value depends on the relative orientations of the magnetizations in the two layers, parallel (P) or anti-parallel (AP). The P state exhibits a low resistance state and the AP state a high resistance state, the resistance can be then measured through the tunnel magneto-resistance effect (TMR). The writing is performed thanks to a current passing in the writing line below the MTJ, inducing the SOT effect. The SOT-MRAM separates the read and write paths avoiding accidentally switching the MTJ while reading. This separation of the write and read paths allows tuning the read and write parameters of the device independently, allowing a large resistance of the stack. For this purpose, two access transistors are added for read and write operations controlled by a Write Word-Line (WWL) and a Read Word-Line (RWL). Given its high resistance, SOT-MRAM may be an important candidate for the implementation of neural networks. Nevertheless, SOT-MRAM like other emerging memristive solutions is also subject to process variation that could impact several characteristics of the MTJ such as the writing time or the TMR. Such discrepancies can alter the desired result. To catch these defects, global variation (+/ -3σ) are added in the SOT-MRAM model on the TMR and the Resistance-Area product (RA), see Fig. 1. In addition to the variations of resistance, the crossbar is also subject to the variation that can impact the selection transistors and periphery like sense amplifiers. We propose in this work to study all those variations and see how they impact the design and the performance of the crossbar array.

III. IMPLEMENTATION

A. SOT-MRAM bit-cell design

In BNNs, the basic computation consists in an XNORbitcount operation. Some studies proposed to implement it with purely digital operation [START_REF] Hirtzlin | Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays[END_REF] [12] [START_REF] Natsui | Design of mtj-based nonvolatile logic gates for quantized neural networks[END_REF] and other studies proposed to stay on the implementation of crossbar by exploiting Ohm and Kirchhoff's laws [START_REF] Sun | Fully parallel RRAM synaptic array for implementing binary neural network with (+1, 1) weights and (+1, 0) neurons[END_REF]. In these implementations of BNN, two complementary cells are used to store a synaptic weight [START_REF] Yu | Neuro-inspired computing with emerging nonvolatile memorys[END_REF]. In the proposed implementation of Fig. 2, the synpatic weight of '1' is stored with a AP and P configuration state, for '0', the configuration is (P-AP). The inputs of the cell (BL) are still differential to allow XNOR operation. In contrast to the 2T-2R implementation [START_REF] Sun | Fully parallel RRAM synaptic array for implementing binary neural network with (+1, 1) weights and (+1, 0) neurons[END_REF], the SOT-MRAM is a 3-terminal device where another NMOS transistor is added to separate the read and write paths. This has the advantage of avoiding read disturb, minimizing errors. To illustrate the XNOR operation in the proposed bit-cell, let's assume that the input is '1' and the wanted weight is '0', in this case AP path will be activated leading to a low current , the output will be equal to AP (0). In the scenario where '1' is the weight and the input is also '1', the P path is chosen leading to a larger current than AP, the output will be '1'(P). The current generated by all the cells in a column will be summed up and compared to a threshold voltage by a sense amplifier (SA) [START_REF] Kobayashi | A current-mode latch sense amplifier and a static power saving input buffer for low-power architecture[END_REF], avoiding the use of an analog to digital converter (ADC). This particular feature of BNN will allow reducing the area as well as the power consumption of the ADC, since the periphery is the block that consumes most of the energy in a crossbar architectures [START_REF] Yu | Neuro-inspired computing with emerging nonvolatile memorys[END_REF], [START_REF] Li | Merging the interface: Power, area and accuracy co-optimization for rram crossbar-based mixed-signal computing system[END_REF].

B. SOT-MRAM-based crossbar array

In this study, a convolution layer implementation is proposed on a crossbar architecture. Prior to that, an algorithmic study with a bitwise CNN was performed on the MNIST dataset. The network architecture is presented in Table I. This model contains 2 convolutional layers, 2 max-pooling layers and 1 FC layer . The output represents here the feature maps. In this paper only Convolution-1 is treated. This layer performs 32 convolutional operations with kernels of 5×5 size. To compute the convolution in an analog fashion, it has to be mapped on a physical NVM-based array. However, mapping a convolutional layer is more challenging than FC layer, single shot mapping is no longer possible. Yet, the convolution realises a dot product between the kernel weights and a specific portion of the input image, several times. Hence a simple method of mapping the convolutional operation is to unroll all the kernels in 1D and arrange them in each column of the crossbar array [START_REF] Gokmen | Training deep convolutional neural networks with resistive cross-point devices[END_REF], [START_REF] Haensch | The next generation of deep learning hardware: Analog computing[END_REF]. For the first convolutional operation, the array contains then 25 inputs (5×5) for 32 outputs. During the inference, only one writing operation of all weights is performed, since they do not change with time.Thus, the weights obtained during the training stage are loaded into the crossbar array.

After the writing step of the weights, several reading steps are carried out for the inference. Indeed, to obtain all the feature maps of the first convolutional layer, 24×24 reading operation per column are required. In order to perform these operations, it is first necessary to encode the MNIST image into reading signals. To do so, the image is sliced in multiple waveform that encode the value of the pixel in the amplitude of the signal, "VDD" for the black pixel and the "GND" for the white one. For both, a reading time of 20 ns with 1 V of amplitude is used to sum all the weights in crossbar and perform the bit-counting operation. Here, the bitwise batch normalization layer explained in Sec. II-C is also added to the crossbar array. The computed thresholds during the training phase (i.e. τ = µ β -

β * √ σ 2 β +ε γ
) are mapped at the circuit level, this will provides several activation levels for the array. To accomplish this mapping, a first estimation of the maximum current generated by the crossbar given the weights in the array is realized. After that, a normalization between the maximum current produced by the array with the maximum threshold obtained through the training is performed. These thresholds are simply represented by a CMOS current reference [START_REF] Singh Chouhan | A 352nw, 30ppm/°c all mos nano ampere current reference circuit[END_REF], with which each column output of the crossbar is compared thanks to a sense amplifier [START_REF] Kobayashi | A current-mode latch sense amplifier and a static power saving input buffer for low-power architecture[END_REF]. In Fig. 3, the crossbar architecture and the CMOS current reference are shown. For clarity reasons, we represent only reading path transistors in the crossbar. In this application, the batch normalization thresholds for the 32 kernels are 9 in total (some kernels have similar values). It is therefore necessary to design 9 different CMOS current references (see Table II).

Nonetheless, process variations can induce an overlap between two references that are close to one another. Thus, two distinct references can generate the same current leading to some reading inaccuracies. An example is shown in Fig. 4 where Monte Carlo (M-C) simulations show an overlap between reference 2 and reference 3. To further evaluate the impact of this overlap, 100 M-C simulations have been performed on each CMOS current reference and the design was accordingly adapted with accurate sizing of the Width/Length (W/L) of the transistors. The aim is to reduce the overlap to the minimum, making it possible to discriminate between the current values. Fig. 5 shows the accuracy of the different references generated for the reading of the crossbar. It indicates the percentage of being in the desired current range according to the chosen current reference (and not in the neighboring current distribution). The studied CMOS references have a low dependency and low variability over process.

C. Crossbar evaluation

To evaluate the accuracy of the proposed crossbar array architecture over manufacturing variations, several reading operations (i.e 200) have been performed based on an image from the MNIST dataset. The objective is to assess the robustness of the crossbar against variations and the impact of accuracy on the desired output. To do so, 100 MC simulations were conducted on the aforementioned reading operations. First, only the CMOS current references were considered. Fig. 6 shows that the output is just slightly affected and we observe only 4% of error over the whole reading operations. Therefore, the robustness of the current references used at the output of the crossbar is confirmed. Secondly, to assess how the distribution of resistance showed in Fig. 1 can impact the desired output, a similar test to the one before is carried out. This time, only the crossbar is taken into consideration. Compared to the previous study, we observe a little increase in the error. This can be explained by the fact that in some worst-case corners, all the bitcells will be impacted in such a way to drastically modify the generated reading current, inducing even more reading errors. Finally, M-C simulations on a combination of the crossbar and the current references were achieved. This time, the error is even more important with a bigger deviation. This case could be seen as the worst case of the study since a decorrelation between the reference current and the current produced by the crossbar array can occur. Although in the latter case the errors are larger, the In Fig. 7, we see the power consumption of the proposed architecture, with a 28 nm technology. The first thing to point out is the low power draw at the crossbar level. By the extremely high resistance of the SOT memory, it is possible to drastically reduce the power consumption in memory. The principal sources of power consumption in this architecture are the decoders and the peripherals. In fact, it is known that the periphery is the part that consumes the most energy in a crossbar architecture. However, the implementation of the activation by means of a current reference and a sense amplifier reduces the energy consumption compared to an ADC [START_REF] Yu | Neuro-inspired computing with emerging nonvolatile memorys[END_REF].

IV. DISCUSSION

In this study, a 1600-component crossbar array was designed using SOT-MRAM for a BNN. To do so, we implemented the XNOR bitcell and performed the BNN computation in memory. In addition, the Batch Normalisation layer was also mapped with current references and sense amplifiers. Then, several M-C simulations were performed to evaluate the impact of process variations on the proposed SOT crossbar array, whether at the bitcell level or on the periphery. For inference implementation, [START_REF] Bocquet | In-memory and error-immune differential rram implementation of binarized deep neural networks[END_REF] presented an RRAM array where they achieved 25 nJ of energy consumption with 2000 Fig. 7: Power consumption figures for the Full Crossbar array implementation RRAM devices. For PCM technology, [START_REF] Ambrogio | Equivalent-accuracy accelerated neural-network training using analogue memory[END_REF] obtained an energy consumption of 56 nJ (with respect to the number of devices). Concerning STT-MRAM technology, [START_REF] Jung | A crossbar array of magnetoresistive memory devices for in-memory computing[END_REF] with a 64×64 crossbar array, obtained a power dissipation of 42 µW. In our implementation, the total power is 3.3 µW and the energy consumption is 65.89 fJ. The implementation presented here allows us to have a consumption significantly lower than what is done in the literature, with a percentage of error remaining similar to other studies [START_REF] Ezzadeen | Low-overhead implementation of binarized neural networks employing robust 2t2r resistive ram bridges[END_REF].

All these studies allow us to consider the viability of using the SOT memory to implement BNN. Indeed, this implementation shows robustness to process variations with a very low percentage of error, 10% on average. More importantly, this memory allows us to circumvent the bottleneck of the STT memory in the conventional crossbar implementation by exploiting its very high resistance [START_REF] Jung | A crossbar array of magnetoresistive memory devices for in-memory computing[END_REF].

V. CONCLUSION

In this paper, we present for the first time the implementation of a BNN with SOT-MRAM technology. We illustrate that this implementation is very energy efficient with a low error rate. For this purpose, we present a binary cell design based on a 4T-2R structure. An evaluation of the energy consumption is performed on the crossbar and the periphery. To show the robustness of the approach, several Monte Carlo simulations have been performed. Nevertheless, the implementation of the crossbar is not complete, the goal here was to evaluate the errors at the circuit level for a future more complete implementation. This will lead to a complete simulation and design flow for evaluation of application.

Fig. 1 :

 1 Fig. 1: Resistance distribution of R P and R AP of SOT-MRAM

Fig. 2 :

 2 Fig. 2: Bitcell containing two complementary SOT-MRAM cells to perform XNOR operation in memory with the truth table

Fig. 3 :

 3 Fig. 3: SOT Crossbar array implementation

Fig. 4 :

 4 Fig. 4: CMOS current reference overlap

Fig. 5 :Fig. 6 :

 56 Fig. 5: Accuracy of the generated references

TABLE I :

 I Binary CNN configuration for MNIST dataset

	Layer name	Kernels	Output
	Convolution-1	5×5×32	24×24×32
	Max Pooling-2	3×3	8×8×32
	Convolution-3	5×5×16	4×4×16
	Max Pooling-4	2×2	2×2×16
	Fully Connected	64×10	10

TABLE II :

 II Value of the CMOS current references

	Reference number	Value (nA)
	0	19
	1	38
	2	57
	3	76
	4	95
	5	114
	6	133
	7	152
	8	190

978-1-6654-5707-1/22/$31.00 ©2022 IEEE