论文标题
精确仿真用于内存计算的回忆横梁阵列
Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing
论文作者
论文摘要
内存计算是一种新兴的非元素Neumann计算范式，其中通过利用内存设备的物理属性来在内存中执行某些计算任务。诸如相位变换内存（PCM）之类的循环设备（以其电导水平存储）特别适合内存计算。特别是，当以横梁配置组织时，可以通过利用Kirchhoff的电路定律来执行矩阵 - 矢量乘法操作。为了探索此类内存计算核心在深度学习以及系统级体系结构探索等应用中的可行性，非常需要开发准确的硬件仿真器，该模拟器可以捕获Memristive设备的关键物理属性。在这里，我们提出了一种用于PCM的模拟器，并使用PCM原型芯片的测量值对其进行了实验验证。此外，我们介绍了模拟器的神经网络推断的应用，我们的模拟器可以很好地捕获大约400,000个PCM设备的电导演变。
In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where information is stored in terms of their conductance levels, are especially well suited for in-memory computing. In particular, memristive devices, when organized in a crossbar configuration can be used to perform matrix-vector multiply operations by exploiting Kirchhoff's circuit laws. To explore the feasibility of such in-memory computing cores in applications such as deep learning as well as for system-level architectural exploration, it is highly desirable to develop an accurate hardware emulator that captures the key physical attributes of the memristive devices. Here, we present one such emulator for PCM and experimentally validate it using measurements from a PCM prototype chip. Moreover, we present an application of the emulator for neural network inference where our emulator can capture the conductance evolution of approximately 400,000 PCM devices remarkably well.
