Face classification using electronic synapses
Institute of Microelectronics · Tsinghua University · +3 more institutions
Abstract
Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using…
Citation impact
- FWCI
- 51.59
- Percentile
- 100%
- References
- 34
Authors
11- PYPeng YaoCorresponding
Institute of Microelectronics, Tsinghua University
- HWHuaqiang Wu
Chinese Institute for Brain Research, Institute of Microelectronics, Shanghai Center for Brain Science and Brain-Inspired Technology, Tsinghua University
- BGBin Gao
Chinese Institute for Brain Research, Institute of Microelectronics, Shanghai Center for Brain Science and Brain-Inspired Technology, Tsinghua University
- SBSukru Burc Eryilmaz
Stanford University
- XHXueyao Huang
Institute of Microelectronics, Tsinghua University
Topics & keywords
- Neuromorphic engineering
- Computer science
- Computer hardware
- Chip
- Xeon Phi
- Efficient energy use
- Resistive random-access memory
- Face (sociological concept)
- Affordable and clean energy