التفاصيل البيبلوغرافية
| العنوان: |
Training a Probabilistic Graphical Model With Resistive Switching Electronic Synapses. |
| المؤلفون: |
Eryilmaz, Sukru Burc1, Wong, Hon-Sum Philip1, Neftci, Emre2, Joshi, Siddharth3, Kim, SangBum4, BrightSky, Matthew4, Lam, Chung4, Lung, Hsiang-Lan5, Cauwenberghs, Gert6 |
| المصدر: |
IEEE Transactions on Electron Devices. Dec2016, Vol. 63 Issue 12, p5004-5011. 8p. |
| مصطلحات موضوعية: |
*DATA mining, HARDWARE design & construction, PHASE change memory, NONVOLATILE random-access memory, SYNAPSES |
| مستخلص: |
Current large-scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. New memory technologies, such as nanoscale two-terminal resistive switching memory devices, offer a compact, scalable, and low-power alternative that permits on-chip colocated processing and memory in fine-grain distributed parallel architecture. Here, we report the first use of resistive memory devices for implementing and training a restricted Boltzmann machine (RBM), a generative probabilistic graphical model as a key component for unsupervised learning in deep networks. We experimentally demonstrate a 45-synapse RBM realized with 90 resistive phase change memory (PCM) elements trained with a bioinspired variant of the contrastive divergence algorithm, implementing Hebbian and anti-Hebbian weight updates. The resistive PCM devices show a twofold to tenfold reduction in error rate in a missing pixel pattern completion task trained over 30 epochs, compared with untrained case. Measured programming energy consumption is 6.1 nJ per epoch with the PCM devices, a factor of ~ 150 times lower than the conventional processor-memory systems. We analyze and discuss the dependence of learning performance on cycle-to-cycle variations and number of gradual levels in the PCM analog memory devices. [ABSTRACT FROM PUBLISHER] |
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| قاعدة البيانات: |
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