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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xinyu Wu Saxena, V. Kehan Zhu |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Boise State Univ., Boise, ID, USA (Xinyu Wu; Saxena, V.; Kehan Zhu) |
| Abstract | Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy-inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1MΩ memristance, and only 13μA current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 948830 |
| Page Count | 6 |
| File Format | |
| ISSN | 21614407 |
| e-ISBN | 9781479919604 |
| DOI | 10.1109/IJCNN.2015.7280819 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-07-12 |
| Publisher Place | Ireland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Memristors CMOS integrated circuits Biology Programming Supervised learning Visualization Spiking neural network Neuromorphic Silicon neuron Memristor |
| Content Type | Text |
| Resource Type | Article |
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