CrossNets: Neuromorphic Hybrid CMOS/Nanoelectronic Networks

Hybrid CMOS/nanoelectronic circuits, combining CMOS chips with simple nanoelectronic crossbar add-ons, may extend the exponential Moore-Law progress of microelectronics beyond the 10-nm frontier. This paper reviews the development of neuromorphic networks (“CrossNets”) based on this prospective technology. In these networks, the neural cell bodies (“somas”) are implemented in the CMOS subsystem, crossbar nanowires are used as axons and dendrites, while two-terminal crosspoint devices are used as elementary synapses. Extensive analysis and simulations have shown that such networks may perform virtually all information processing tasks demonstrated with software-implemented neural networks, with much higher performance. Estimates show that CrossNets may eventually overcome bio-cortical circuits in density, at comparable connectivity, while operating 4 to 6 orders of magnitude faster, at manageable power dissipation.

[1]  P. C. Murphy,et al.  Cerebral Cortex , 2017, Cerebral Cortex.

[2]  O. Turel,et al.  Possible nanoelectronic implementation of neuromorphic networks , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[3]  電子情報通信学会.  IEICE electronics express , 2004 .

[4]  Jianbo Lu,et al.  polymers for advanced technologies , 2009 .

[5]  Dmitri B. Strukov,et al.  A reconfigurable architecture for hybrid CMOS/Nanodevice circuits , 2006, FPGA '06.

[6]  D. Strukov,et al.  CMOL: Devices, Circuits, and Architectures , 2006 .

[7]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[8]  J. M. Oshorn.  Proc. Nat. Acad. Sei , 1978 .

[9]  Dmitri B. Strukov,et al.  Prospects for the development of digital CMOL circuits , 2007, 2007 IEEE International Symposium on Nanoscale Architectures.

[10]  J. Gilman,et al.  Nanotechnology , 2001 .

[11]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[12]  Paul E. Hasler,et al.  Floating gate synapses with spike time dependent plasticity , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[13]  M. Carpenter.  The cerebral cortex , 1976 .

[14]  Richard S. Sutton,et al.  Reinforcement Learning , 1992, Handbook of Machine Learning.

[15]  S. Folling,et al.  Single-electron latching switches as nanoscale synapses , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[16]  Jung Hoon Lee,et al.  In Situ Training of CMOL CrossNets , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[17]  Ericka Stricklin-Parker,et al.  Ann , 2005 .

[18]  Gregory S. Snider,et al.  Spike-timing-dependent learning in memristive nanodevices , 2008, 2008 IEEE International Symposium on Nanoscale Architectures.

[19]  Yann LeCun,et al.  The mnist database of handwritten digits , 2005 .

[20]  Bruno A. Olshausen,et al.  Book Review , 2003, Journal of Cognitive Neuroscience.

[21]  Xiaolong Ma,et al.  CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits , 2005, NIPS.

[22]  K. Richter,et al.  Introducing Molecular Electronics , 2005 .

[23]  I. Baek,et al.  Multi-layer cross-point binary oxide resistive memory (OxRRAM) for post-NAND storage application , 2005, IEEE InternationalElectron Devices Meeting, 2005. IEDM Technical Digest..

[24]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[25]  S. Tam,et al.  An electrically trainable artificial neural network (ETANN) with 10240 'floating gate' synapses , 1990, International 1989 Joint Conference on Neural Networks.