{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T19:57:48Z","timestamp":1701460668585},"reference-count":30,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"20","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Electron. Express"],"published-print":{"date-parts":[[2020,10,25]]},"DOI":"10.1587\/elex.17.20200273","type":"journal-article","created":{"date-parts":[[2020,10,5]],"date-time":"2020-10-05T22:10:59Z","timestamp":1601935859000},"page":"20200273-20200273","source":"Crossref","is-referenced-by-count":3,"title":["Current injection: a hardware method of adapting non-ideal effects of ReRAM based deep learning accelerator"],"prefix":"10.1587","volume":"17","author":[{"given":"Gengxin","family":"Liu","sequence":"first","affiliation":[{"name":"Dept. Communication and Information Engineering, Chengdu College of University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manqing","family":"Hu","sequence":"additional","affiliation":[{"name":"Dept. Communication and Information Engineering, Chengdu College of University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yitian","family":"Zhang","sequence":"additional","affiliation":[{"name":"CloudMinds Technologies Chengdu Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E","family":"Du","sequence":"additional","affiliation":[{"name":"Dept. Communication and Information Engineering, Chengdu College of University of Electronic Science and Technology of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dekang","family":"Liu","sequence":"additional","affiliation":[{"name":"Chongqing Jinmei Communication Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] S.-L. Liu, <i>et al.<\/i>: \u201cScaling the \u201cmemory wall\u201d: designer track,\u201d IEEE\/ACM International Conference on Computer-Aided Design (2012) 271 (DOI: 10.1145\/2429384.2429437)."},{"key":"2","unstructured":"[2] S.W. Keckler, <i>et al.<\/i>: \u201cGPUs and the future of parallel computing,\u201d IEEE Micro <b>31<\/b> (2011) 7 (DOI: 10.1109\/MM.2011.89)."},{"key":"3","unstructured":"[3] L. Song, <i>et al.<\/i>: \u201cPipeLayer: a pipelined ReRAM-based accelerator for deep learning,\u201d IEEE International Symposium on High Performance Computer Architecture (HPCA) (2017) 541 (DOI: 10.1109\/HPCA.2017.55)."},{"key":"4","unstructured":"[4] P. Chi, <i>et al.<\/i>: \u201cPRIME: a novel processing-in-memory architecture for neural network computation in ReRAM-based main memory,\u201d ACM SIGARCH Computer Architecture News <b>44<\/b> (2016) 27 (DOI: 10.1145\/3007787.3001140)."},{"key":"5","unstructured":"[5] S. Li, <i>et al.<\/i>: \u201cPinatubo: a processing-in-memory architecture for bulk bitwise operations in emerging non-volatile memories,\u201d Proceedings of the 53rd Annual Design Automation Conference (2016) 1 (DOI: 10.1145\/2897937.2898064)."},{"key":"6","unstructured":"[6] J. Lin and J.-S. Yuan: \u201cA scalable and reconfigurable in-memory architecture for ternary deep spiking neural network with ReRAM based neurons,\u201d Neurocomputing <b>375<\/b> (2020) 102 (DOI: 10.1016\/j.neucom.2019.09.082)."},{"key":"7","unstructured":"[7] J.-S. Yuan, <i>et al.<\/i>: \u201cUltra-low-power design and hardware security using emerging technologies for internet of things,\u201d Electronics <b>6<\/b> (2017) 67 (DOI: 10.3390\/electronics6030067)."},{"key":"8","unstructured":"[8] Z. He, <i>et al.<\/i>: \u201cNoise injection adaption: end-to-end ReRAM crossbar non-ideal effect adaption for neural network mapping,\u201d Proceedings of the 56th Annual Design Automation Conference 2019 (2019) 1 (DOI: 10.1145\/3316781.3317870)."},{"key":"9","unstructured":"[9] I. Chakraborty, <i>et al.<\/i>: \u201cTechnology aware training in memristive neuromorphic systems for nonideal synaptic crossbars,\u201d IEEE Trans. Emerg. Topics Comput. Intell. <b>2<\/b> (2018) 335 (DOI: 10.1109\/TETCI.2018.2829919)."},{"key":"10","unstructured":"[10] L. Chen, <i>et al.<\/i>: \u201cAccelerator-friendly neural-network training: learning variations and defects in RRAM crossbar,\u201d Proceedings of the Conference on Design, Automation &amp; Test in Europe (2017) 19 (DOI: 10.23919\/DATE.2017.7926952)."},{"key":"11","unstructured":"[11] Y. Zhang, <i>et al.<\/i>: \u201cCACF: a novel circuit architecture co-optimization framework for improving performance, reliability and energy of ReRAM-based main memory system,\u201d ACM Trans. Archit. Code Op. (TACO) <b>15<\/b> (2018) 1 (DOI: 10.1145\/3195799)."},{"key":"12","unstructured":"[12] N.P. Jouppi, <i>et al.<\/i>: \u201cIn-datacenter performance analysis of a tensor processing unit,\u201d Proceedings of the 44th Annual International Symposium on Computer Architecture (2017) 1 (DOI: 10.1145\/3079856.3080246)."},{"key":"13","unstructured":"[13] C. Xu, <i>et al.<\/i>: \u201cOvercoming the challenges of crossbar resistive memory architectures,\u201d 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA) (2015) 476 (DOI: 10.1109\/HPCA.2015.7056056)."},{"key":"14","unstructured":"[14] L. Xia, <i>et al.<\/i>: \u201cStuck-at fault tolerance in rram computing systems,\u201d IEEE J. Emerg. Sel. Topics Circuits Syst. <b>8<\/b> (2017) 102 (DOI: 10.1109\/JETCAS.2017.2776980)."},{"key":"15","unstructured":"[15] S. Mittal: \u201cA survey of ReRAM-based architectures for processing-in-memory and neural networks,\u201d Machine Learning and Knowledge Extraction <b>1<\/b> (2019) 75 (DOI: 10.3390\/make1010005)."},{"key":"16","unstructured":"[16] C.-Y. Chen, <i>et al.<\/i>: \u201cRRAM defect modeling and failure analysis based on march test and a novel squeeze-search scheme,\u201d IEEE Trans. Comput. <b>64<\/b> (2015) 180 (DOI: 10.1109\/TC.2014.12)."},{"key":"17","unstructured":"[17] B. Liu, <i>et al.<\/i>: \u201cReduction and IR-drop compensations techniques for reliable neuromorphic computing systems,\u201d Proceedings of the 2014 IEEE\/ACM International Conference on Computer-Aided Design (2014) 63 (DOI: 10.1109\/ICCAD.2014.7001330)."},{"key":"18","unstructured":"[18] Z. Jiang, <i>et al.<\/i>: \u201cVerilog-a compact model for oxide-based resistive random access memory (RRAM),\u201d 2014 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD) (2014) 41 (DOI: 10.1109\/SISPAD.2014.6931558)."},{"key":"19","unstructured":"[19] J. Lin and J.-S. Yuan: \u201cAnalysis and simulation of capacitor-less ReRAM-based stochastic neurons for the in-memory spiking neural network,\u201d IEEE Trans. Biomed. Circuits Syst. <b>12<\/b> (2018) 1004 (DOI: 10.1109\/TBCAS.2018.2843286)."},{"key":"20","unstructured":"[20] J. Lin and J.-S. Yuan: \u201cCapacitor-less RRAM-based stochastic neuron for event-based unsupervised learning,\u201d 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS) (2017) 1 (DOI: 10.1109\/BIOCAS.2017.8325169)."},{"key":"21","unstructured":"[21] S. Yu, <i>et al.<\/i>: \u201cStochastic learning in oxide binary synaptic device for neuromorphic computing,\u201d Frontiers in neuroscience <b>7<\/b> (2013) 186 (DOI: 10.3389\/fnins.2013.00186)."},{"key":"22","unstructured":"[22] R. Naous, <i>et al.<\/i>: \u201cStochasticity modeling in memristors,\u201d IEEE Trans. Nanotechnol. <b>15<\/b> (2015) 15 (DOI: 10.1109\/TNANO.2015.2493960)."},{"key":"23","unstructured":"[23] B. Rueckauer, <i>et al.<\/i>: \u201cConversion of continuous-valued deep networks to efficient event-driven networks for image classification,\u201d Frontiers in neuroscience <b>11<\/b> (2017) 682 (DOI: 10.3389\/fnins.2017.00682)."},{"key":"24","unstructured":"[24] S. Yu, <i>et al.<\/i>: \u201cScaling-up resistive synaptic arrays for neuro-inspired architecture: Challenges and prospect,\u201d 2015 IEEE International Electron Devices Meeting (IEDM) (2015) 17.3.1 (DOI: 10.1109\/IEDM.2015.7409718)."},{"key":"25","unstructured":"[25] F. Alibart, <i>et al.<\/i>: \u201cHigh precision tuning of state for memristive devices by adaptable variation-tolerant algorithm,\u201d Nanotechnology <b>23<\/b> (2012) 075201 (DOI: 10.1088\/0957-4484\/23\/7\/075201)."},{"key":"26","unstructured":"[26] C. Xu, <i>et al.<\/i>: \u201cUnderstanding the trade-offs in multi-level cell ReRAM memory design,\u201d Design Automation Conference (DAC) (2013) 1 (DOI: 10.1145\/2463209.2488867)."},{"key":"27","unstructured":"[27] K. He, <i>et al.<\/i>: \u201cDeep residual learning for image recognition,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 770 (DOI: 10.1109\/cvpr.2016.90)."},{"key":"28","unstructured":"[28] Y. LeCun, <i>et al.<\/i>: \u201cGradient-based learning applied to document recognition,\u201d Proc. IEEE <b>86<\/b> (1998) 2278 (DOI: 10.1109\/5.726791)."},{"key":"29","unstructured":"[29] J. Deng, <i>et al.<\/i>: \u201cImageNet: a large-scale hierarchical image database,\u201d 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009) 248 (DOI: 10.1109\/CVPR.2009.5206848)."},{"key":"30","unstructured":"[30] B. Zhang, <i>et al.<\/i>: \u201cHandling stuck-at-faults in memristor crossbar arrays using matrix transformations,\u201d Proceedings of the 24th Asia and South Pacific Design Automation Conference (2019) 438 (DOI: 10.1145\/3287624.3287707)."}],"container-title":["IEICE Electronics Express"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/elex\/17\/20\/17_17.20200273\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T03:34:42Z","timestamp":1604115282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/elex\/17\/20\/17_17.20200273\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,25]]},"references-count":30,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.1587\/elex.17.20200273","relation":{},"ISSN":["1349-2543"],"issn-type":[{"value":"1349-2543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,25]]}}}