计算RAM（CRAM）中尖峰神经网络的推理与学习引擎（CS ET）---蔡秋纯

尖峰神经网络（SNN）是一种受生物启发的计算模型，能够模拟人脑和类脑结构中的神经计算。主要承诺是非常低的能源消耗。不幸的是，基于Von Neumann体系结构的SNN加速器常常无法有效地满足大规模的计算和数据传输需求。在这项工作中，我们提出了一种很有前途的替代方案，一种基于Spintronic计算RAM（CRAM）的存储器内SNN加速器，它克服了可扩展性的限制，与典型的ASIC解决方案相比，可以减少高达164.1倍的能耗。

原文标题：An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM)

原文：Spiking Neural Networks (SNN) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise is very low energy consumption. Unfortunately, classic Von Neumann architecture based SNN accelerators often fail to address demanding computation and data transfer requirements efficiently at scale. In this work, we propose a promising alternative, an in-memory SNN accelerator based on Spintronic Computational RAM (CRAM) to overcome scalability limitations, which can reduce the energy consumption by up to 164.1× when compared to a representative ASIC solution.

原文作者：Hüsrev Cılasun, Salonik Resch, Zamshed I. Chowdhury, Erin Olson, Masoud Zabihi, Zhengyang Zhao, Thomas Peterson, Keshab Parhi, Jian-Ping Wang, Sachin S. Sapatnekar, Ulya Karpuzcu

原文地址：https://arxiv.org/abs/2006.03007