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The Advantages of a DRAM-Based Digital Architecture for Low-Power, Large-Scale Neuro-Chips

Takao WATANABE, Masakazu AOKI, Katsutaka KIMURA, Takeshi SAKATA, Kiyoo ITOH

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Summary :

The advantages of a neuro-chip architecture based on a DRAM are demonstrated through a discussion of the general issuse regarding a memory based neuro-chip architecture and a comparison with a chip based on an SRAM. The performance of both chips is compared assuming digital operation, a 1.5-V supply voltage, a 106-synapse neural network capability, and a 0.5-µm CMOS design rule. The use of a one-transistor DRAM cell array for the storage of synapse weights results in a chip 55% smaller than an SRAM based chip with the same 8-Mbit memory capacity and the same number of processing elements. No additional operations for refreshing the DRAM cell array are necessary during the processing of the neural networks. This is because all the synapse weights in the array are transferred to the processing elements during the processing and the DRAM cells in the array are automatically refreshed when they are selected. The precharge operation of the DRAM cell array degrades the processing speed, however a processing speed of 1.37 GCPS is expected for the DRAM based chip. That speed is comparable to the 1.71 GCPS for the SRAM based chip with the same 256 parallel-processing elements. A DRAM cell array has the additional advantage of lower power dissipation in this specific usage for the neuro-chip. The dynamic operation of the DRAM cell array results in a 10% lower operating power dissipation than a chip using an SRAM cell array at the same processing speed of 1.37 GCPS. That lower operating power dissipation enables a DRAM based chip to run on a 1.5-V dry cell for longer under intermittent daily use even though the SRAM cell array has little power dissipation in data-holding mode.

Publication
IEICE TRANSACTIONS on Electronics Vol.E76-C No.7 pp.1206-1214
Publication Date
1993/07/25
Publicized
Online ISSN
DOI
Type of Manuscript
Special Section PAPER (Special Issue on New Architecture LSIs)
Category
Neural Networks and Chips

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