ABSTRACT
Neuromorphic computing is a revolutionary approach of computation, which attempts to mimic the human brain's mechanism for extremely high implementation efficiency and intelligence. Latest research studies showed that the memristor technology has a great potential for realizing power- and area-efficient neuromorphic computing systems (NCS). On the other hand, the memristor device processing is still under development. Unreliable devices can severely degrade system performance, which arises as one of the major challenges in developing memristor-based NCS. In this paper, we first review the impacts of the limited reliability of memristor devices and summarize the recent research progress in building reliable and efficient memristor-based NCS. In the end, we discuss the main difficulties and the trend in memristor-based NCS development.
- Bing Chen, Fuxi Cai, Jiantao Zhou, Wen Ma, Patrick Sheridan, and Wei D Lu. Efficient in-memory computing architecture based on crossbar arrays. In 2015 IEEE International Electron Devices Meeting (IEDM), pages 17--5, 2015.Google ScholarCross Ref
- Miao Hu, John Paul Strachan, Zhiyong Li, Emmanuelle M Grafals, Noraica Davila, Catherine Graves, Sitty Lam, Ning Ge, Jianhua Joshua Yang, and R Stanley Williams. Dot-product engine for neuromorphic computing: programming 1t1m crossbar to accelerate matrix-vector multiplication. In 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC), pages 1--6, 2016. Google ScholarDigital Library
- B. Li, L. Song, F. Chen, X. Qian, Y. Chen, and H. H. Li. Reram-based accelerator for deep learning. In 2018 Design, Automation Test in Europe Conference Exhibition (DATE), pages 815--820, March 2018.Google Scholar
- Dimin Niu, Cong Xu, Naveen Muralimanohar, Norman P Jouppi, and Yuan Xie. Design of cross-point metal-oxide reram emphasizing reliability and cost. In 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pages 17--23, 2013. Google ScholarDigital Library
- Annie Foong and Frank Hady. Storage as fast as rest of the system. In 2016 IEEE 8th International Memory Workshop (IMW), pages 1--4, 2016.Google ScholarCross Ref
- Chenchen Liu, Bonan Yan, Chaofei Yang, Linghao Song, Zheng Li, Beiye Liu, Yiran Chen, Hai Li, Qing Wu, and Hao Jiang. A spiking neuromorphic design with resistive crossbar. In 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), pages 1--6, 2015. Google ScholarDigital Library
- Beiye Liu, Hai Li, Yiran Chen, Xin Li, Tingwen Huang, Qing Wu, and Mark Barnell. Reduction and ir-drop compensations techniques for reliable neuromorphic computing systems. In 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pages 63--70, 2014. Google ScholarDigital Library
- Chenchen Liu, Miao Hu, John Paul Strachan, and Hai Li. Rescuing memristor-based neuromorphic design with high defects. In Design Automation Conference (DAC), 2017 54th ACM/EDAC/IEEE, pages 1--6. IEEE, 2017. Google ScholarDigital Library
- Ting Chang, Sung-Hyun Jo, and Wei Lu. Short-term memory to long-term memory transition in a nanoscale memristor. ACS Nano, 5(9):7669--7676, 2011.Google ScholarCross Ref
- Bonan Yan, Jianhua (Joshua) Yang, Qing Wu, Yiran Chen, and Hai (Helen) Li. A closed-loop design to enhance weight stability of memristor based neural network chips. In Proceedings of the 36th International Conference on Computer-Aided Design, ICCAD '17, pages 541--548, Piscataway, NJ, USA, 2017. IEEE Press. Google ScholarDigital Library
- Yiran Chen, Hai Li, Xiaobin Wang, Wenzhong Zhu, Wei Xu, and Tong Zhang. A nondestructive self-reference scheme for spin-transfer torque random access memory (stt-ram). In 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 148--153, 2010. Google ScholarDigital Library
- Dimin Niu, Yang Xiao, and Yuan Xie. Low power memristor-based reram design with error correcting code. In 2012 17th Asia and South Pacific Design Automation Conference (ASP-DAC), pages 79--84, 2012.Google ScholarCross Ref
- Manqing Mao, Pai-Yu Chen, Shimeng Yu, and Chaitali Chakrabarti. A multilayer approach to designing energy-efficient and reliable reram cross-point array system. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25(5):1611--1621, 2017. Google ScholarDigital Library
- Amirali Ghofrani, Miguel Angel Lastras-Montaño, and Kwang-Ting Cheng. Towards data reliable crossbar-based memristive memories. In Test Conference (ITC), 2013 IEEE International, pages 1--10. IEEE, 2013.Google ScholarCross Ref
- Leon Chua. Memristor-the missing circuit element. IEEE Transactions on Circuit Theory, 18(5):507--519, 1971.Google ScholarCross Ref
- Dmitri B Strukov, Gregory S Snider, Duncan R Stewart, and R Stanley Williams. The missing memristor found. Nature, 453(7191):80--83, 2008.Google ScholarCross Ref
- Cong Xu, Dimin Niu, Naveen Muralimanohar, Rajeev Balasubramonian, Tao Zhang, Shimeng Yu, and Yuan Xie. Overcoming the challenges of crossbar resistive memory architectures. In 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA), pages 476--488. IEEE, 2015.Google ScholarCross Ref
- Cong Xu, Xiangyu Dong, Norman P Jouppi, and Yuan Xie. Design implications of memristor-based rram cross-point structures. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2011, pages 1--6. IEEE, 2011.Google Scholar
- Miao Hu, Hai Li, Yiran Chen, Qing Wu, and Garrett S Rose. Bsb training scheme implementation on memristor-based circuit. In Computational Intelligence for Security and Defense Applications (CISDA), 2013 IEEE Symposium on, pages 80--87. IEEE, 2013.Google Scholar
- Pai-Yu Chen and Shimeng Yu. Compact modeling of rram devices and its applications in 1t1r and 1s1r array design. IEEE Transactions on Electron Devices, 62(12):4022--4028, 2015.Google ScholarCross Ref
- John Paul Strachan, Antonio C Torrezan, Feng Miao, Matthew D Pickett, J Joshua Yang, Wei Yi, Gilberto Medeiros-Ribeiro, and R Stanley Williams. State dynamics and modeling of tantalum oxide memristors. IEEE Transactions on Electron Devices, 60(7):2194--2202, 2013.Google ScholarCross Ref
- S. Li, W. Wen, Y. Wang, S. Han, Y. Chen, and H. Li. An fpga design framework for cnn sparsification and acceleration. In 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pages 28--28, April 2017.Google ScholarCross Ref
- Indranil Chakraborty, Deboleena Roy, and Kaushik Roy. Technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(5):335--344, 2018.Google ScholarCross Ref
- Amr Tosson, Shimeng Yu, Mohab Anis, and Lan Wei. Mitigating the effect of reliability soft-errors of rram devices on the performance of rram-based neuromorphic systems. In Proceedings of the on Great Lakes Symposium on VLSI 2017, pages 53--58. ACM, 2017. Google ScholarDigital Library
- Chaofei Yang, Beiye Liu, Hai Li, Yiran Chen, Wujie Wen, Mark Barnell, Qing Wu, and Jeyavijayan Rajendran. Security of neuromorphic computing: thwarting learning attacks using memristor's obsolescence effect. In Proceedings of the 35th International Conference on Computer-Aided Design, page 97, 2016. Google ScholarDigital Library
- An Chen and Ming-Ren Lin. Variability of resistive switching memories and its impact on crossbar array performance. In Reliability Physics Symposium (IRPS), 2011 IEEE International, pages MY-7. IEEE, 2011.Google ScholarCross Ref
- Wenqin Huangfu, Lixue Xia, Ming Cheng, Xiling Yin, Tianqi Tang, Boxun Li, Krishnendu Chakrabarty, Yuan Xie, Yu Wang, and Huazhong Yang. Computation-oriented fault-tolerance schemes for rram computing systems. In ASP-DAC, pages 794--799, 2017.Google ScholarCross Ref
- Lixue Xia, Mengyun Liu, Xuefei Ning, Krishnendu Chakrabarty, and Yu Wang. Fault-tolerant training with on-line fault detection for rram-based neural computing systems. In Proceedings of the 54th Annual Design Automation Conference 2017, page 33. ACM, 2017. Google ScholarDigital Library
- L. Xia, W. Huangfu, T. Tang, X. Yin, K. Chakrabarty, Y. Xie, Y. Wang, and H. Yang. Stuck-at fault tolerance in rram computing systems. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 8(1):102--115, March 2018.Google ScholarCross Ref
- Pai-Yu Chen, Deepak Kadetotad, Zihan Xu, Abinash Mohanty, Binbin Lin, Jieping Ye, Sarma Vrudhula, Jae-sun Seo, Yu Cao, and Shimeng Yu. Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip. In Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, pages 854--859. EDA Consortium, 2015. Google ScholarDigital Library
- X. Liu, M. Mao, B. Liu, B. Li, Y. Wang, H. Jiang, M. Barnell, Q. Wu, J. Yang, H. Li, and Y. Chen. Harmonica: A framework of heterogeneous computing systems with memristor-based neuromorphic computing accelerators. IEEE Transactions on Circuits and Systems I: Regular Papers, 63(5):617--628, 2016.Google ScholarCross Ref
- Mirko Prezioso, Farnood Merrikh-Bayat, BD Hoskins, GC Adam, Konstantin K Likharev, and Dmitri B Strukov. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 521(7550):61, 2015.Google ScholarCross Ref
- Joongho Choi and Bing J Sheu. A high-precision vlsi winner-take-all circuit for self-organizing neural networks. IEEE Journal of Solid-state circuits, 28(5):576--584, 1993.Google ScholarCross Ref
Recommendations
Learning Behavior of Memristor-Based Neuromorphic Circuits in the Presence of Radiation
ICONS '19: Proceedings of the International Conference on Neuromorphic SystemsIn this paper, a feed-forward spiking neural network with memristive synapses is designed to learn a spatio-temporal pattern representing the 25-pixel character 'B' by separating correlated and uncorrelated afferents. The network uses spike-timing-...
Neuromorphic Computing Based on Memristor
ICASIT 2020: Proceedings of the 2020 International Conference on Aviation Safety and Information TechnologyBioelectronics is defined as a combination of electronic principles and biological phenomena. While many major fields such as Medical treatment, technology advancement, healthcare are increasing its demand for bioelectronic circuits. And the ...
Reconfigurable Neuromorphic Computing System with Memristor-Based Synapse Design
Conventional CMOS technology is slowly approaching its physical limitations and researchers are increasingly utilizing nanotechnology to both extend CMOS capabilities and to explore potential replacements. Novel memristive systems continue to attract ...
Comments