A CMOS Spiking Neuron for Brain-Inspired Neural Networks With Resistive Synapses and In Situ Learning
作者： Xinyu Wu , Vishal Saxena , Kehan Zhu , Sakkarapani Balagopal
DOI: 10.1109/TCSII.2015.2456372
关键词: Resistive touchscreen 、 Computer science 、 Spiking neural network 、 Electrical engineering 、 Synapse 、 Neuromorphic engineering 、 CMOS 、 Artificial neural network 、 Electronic engineering 、 Associative learning 、 Resistive random-access memory
摘要: Nanoscale resistive memory devices are expected to fuel dense integration of electronic synapses for large-scale neuromorphic systems. To realize such a brain-inspired computing chip, compact CMOS spiking neuron that performs in situ learning and while driving large number is desired. This brief presents novel leaky integrate-and-fire design implements the dual-mode operation current synaptic drive, with single operational amplifier (opamp) enables crossbar synapses. The proposed was implemented 0.18- $\mu\mbox{m}$ technology. Measurements show neuron's ability drive thousand demonstrate associative learning. circuit occupies small area 0.01 mm 2 has an energy efficiency value 9.3 pJ/spike/synapse.
harvard.edu
sci-hub.se 参考文章(17)
Mostafa Rahimi Azghadi, Nicolangelo Iannella, Said F. Al-Sarawi, Giacomo Indiveri, Derek Abbott, Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges Proceedings of the IEEE. ,vol. 102, pp. 717- 737 ,(2014) , 10.1109/JPROC.2014.2314454
T. Serrano-Gotarredona, B. Linares-Barranco, Design of adaptive nano/CMOS neural architectures international conference on electronics, circuits, and systems. pp. 949- 952 ,(2012) , 10.1109/ICECS.2012.6463504
Gwendal Lecerf, Jean Tomas, Sylvain Saighi, Excitatory and Inhibitory Memristive Synapses for Spiking Neural Networks international symposium on circuits and systems. pp. 1616- 1619 ,(2013) , 10.1109/ISCAS.2013.6572171
Gwendal Lecerf, Jean Tomas, Soren Boyn, Stephanie Girod, Ashwin Mangalore, Julie Grollier, Sylvain Saighi, Silicon neuron dedicated to memristive spiking neural networks international symposium on circuits and systems. pp. 1568- 1571 ,(2014) , 10.1109/ISCAS.2014.6865448
Ting Chang, Yuchao Yang, Wei Lu, Building Neuromorphic Circuits with Memristive Devices IEEE Circuits and Systems Magazine. ,vol. 13, pp. 56- 73 ,(2013) , 10.1109/MCAS.2013.2256260
Idongesit E. Ebong, Pinaki Mazumder, CMOS and Memristor-Based Neural Network Design for Position Detection Proceedings of the IEEE. ,vol. 100, pp. 2050- 2060 ,(2012) , 10.1109/JPROC.2011.2173089
Runchun Wang, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik, A generalised conductance-based silicon neuron for large-scale spiking neural networks international symposium on circuits and systems. pp. 1564- 1567 ,(2014) , 10.1109/ISCAS.2014.6865447
Wei He, Kejie Huang, Ning Ning, Kiruthika Ramanathan, Guoqi Li, Yu Jiang, JiaYin Sze, Luping Shi, Rong Zhao, Jing Pei, Enabling an integrated rate-temporal learning scheme on memristor. Scientific Reports. ,vol. 4, pp. 4755- 4755 ,(2015) , 10.1038/SREP04755
J. M. Cruz-Albrecht, M. W. Yung, N. Srinivasa, Energy-Efficient Neuron, Synapse and STDP Integrated Circuits IEEE Transactions on Biomedical Circuits and Systems. ,vol. 6, pp. 246- 256 ,(2012) , 10.1109/TBCAS.2011.2174152
Sylvain Saïghi, Christian G. Mayr, Teresa Serrano-Gotarredona, Heidemarie Schmidt, Gwendal Lecerf, Jean Tomas, Julie Grollier, Sören Boyn, Adrien F. Vincent, Damien Querlioz, Selina La Barbera, Fabien Alibart, Dominique Vuillaume, Olivier Bichler, Christian Gamrat, Bernabé Linares-Barranco, Plasticity in memristive devices for spiking neural networks Frontiers in Neuroscience. ,vol. 9, pp. 51- 51 ,(2015) , 10.3389/FNINS.2015.00051