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Face classification using electronic synapses.
- Source :
- Nature Communications; May2017, Vol. 8 Issue 5, p15199, 1p
- Publication Year :
- 2017
-
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 Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20411723
- Volume :
- 8
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Nature Communications
- Publication Type :
- Academic Journal
- Accession number :
- 123379364
- Full Text :
- https://doi.org/10.1038/ncomms15199