Implementation challenges for scalable neuromorphic computing

S. Yamamichi, A. Horibe, T. Aoki, K. Hosokawa, T. Hisada, H. Mori
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Abstract

In the big data era, a new computing system, called Cognitive Computing, that can handle unstructured data, learn and extract the insights is required. A neuromorphic device is a key component for this, and several architectures are reported. Compared to the neuromorphic device with SRAM-based spiking neural network, a cross-bar structure device realizes on-chip leaning, but requires high-density off-chip interconnect, much higher than those for conventional high-end logic devices. Recent progress of solder bumping and 3-dimentional integration technologies are described.
可扩展神经形态计算的实现挑战
在大数据时代,需要一种新的计算系统,称为认知计算,它可以处理非结构化数据,学习并提取见解。神经形态装置是其中的关键组成部分,并报道了几种结构。与基于sram的尖峰神经网络的神经形态器件相比,交叉棒结构器件实现了片上学习,但需要高密度的片外互连,远高于传统高端逻辑器件。介绍了焊料碰撞和三维集成技术的最新进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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