Open AccessParallel Programming of Resistive Cross-point Array for Synaptic Plasticity
Author(s) -
Zihan Xu,
Abinash Mohanty,
Pai-Yu Chen,
Deepak Kadetotad,
Binbin Lin,
Jieping Ye,
Sarma Vrudhula,
Shimeng Yu,
Jae-sun Seo,
Yu Cao
Publication year - 2014
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2014.11.094
Subject(s) - resistive random access memory , computer science , neuromorphic engineering , speedup , node (physics) , synaptic plasticity , spike (software development) , voltage , parallel computing , artificial intelligence , artificial neural network , electrical engineering , acoustics , biochemistry , chemistry , physics , receptor , software engineering , engineering
This paper proposes a parallel programming scheme for the cross-point array with resistive random access memory (RRAM). Synaptic plasticity in unsupervised learning is realized by tuning the conductance of each RRAM cell. Inspired by the spike-timing-dependent-plasticity (STDP), the programming strength is encoded into the spike firing rate (i.e., pulse frequency) and the overlap time (i.e., duty cycle) of the pre-synaptic node and post-synaptic node, and simultaneously applied to all RRAM cells in the cross-point array. Such an approach achieves parallel programming of the entire RRAM array, only requiring local information from pre-synaptic and post-synaptic nodes to each RRAM cell. As demonstrated by digital peripheral circuits implemented in 65nm CMOS, the programming time of a 40kb RRAM array is 84ns, indicating 900X speedup as compared to state-of-the-art software approach of sparse coding in image feature extraction
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