论文标题
计算RAM中尖峰神经网络的推理和学习引擎（CRAM）
An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM)
论文作者
论文摘要
尖峰神经网络（SNN）代表了一个生物学启发的计算模型，能够模拟人脑和脑样结构中的神经计算。主要的承诺是能源消耗非常低。不幸的是，经典的von Neumann架构基于SNN加速器通常无法在大规模上有效地解决要求的计算和数据传输要求。在这项工作中，我们提出了一种有希望的替代方案，一种基于自旋计算RAM（CRAM）的内存中SNN加速器，以克服可伸缩性限制，与代表性的ASIC解决方案相比，可以将能源消耗降低到164.1 $ \ times $。
Spiking Neural Networks (SNN) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise is very low energy consumption. Unfortunately, classic Von Neumann architecture based SNN accelerators often fail to address demanding computation and data transfer requirements efficiently at scale. In this work, we propose a promising alternative, an in-memory SNN accelerator based on Spintronic Computational RAM (CRAM) to overcome scalability limitations, which can reduce the energy consumption by up to 164.1$\times$ when compared to a representative ASIC solution.
