An area-efficient multiply-accumulation architecture and implementations for time-domain neural processing

Ichiro Kawashima, Yuichi Katori, T. Morie, H. Tamukoh
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引用次数: 2

Abstract

In our work, a new area-efficient multiply-accumulation scheme for time-domain neural processing named differential multiply-accumulation is proposed. Our new scheme reduces hardware resources utilization of multiply-accumulation with suppressing the increasing computational time resulting from the time-multiplexing. As a result, 2,048 neurons of fully connected CBM and RC-CBM were synthesized for a single field-programmable gate array (FPGA).
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时域神经处理的面积效率倍增累积架构与实现
在我们的工作中,提出了一种新的时域神经处理的面积高效的乘法-积累方案,称为微分乘法-积累。新方案通过抑制时间复用带来的计算时间的增加,降低了乘法累加的硬件资源利用率。结果表明,在单个现场可编程门阵列(FPGA)上合成了2,048个全连接CBM和RC-CBM神经元。
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