Electrical static random memory (E-SRAM) is the current standard for internal static memory in Field Programmable Gate Array (FPGA). Despite the dramatic improvement in E-SRAM technology over the past decade, the goal of ultra-fast, energy-efficient static random memory has yet to be achieved with E-SRAM technology. However, preliminary research into optical static random access memory (O-SRAM) has shown promising results in creating energy-efficient ultra-fast static memories. This paper investigates the advantage of O-SRAM over E-SRAM in access speed and energy performance while executing sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP). spMTTKRP is an essential component of tensor decomposition algorithms which is heavily used in data science applications. The evaluation results show O-SRAMs can achieve speeds of 1.1x - 2.9x while saving 2.8x - 8.1x energy compared to conventional E-SRAM technology.
翻译:电动静态随机内存(E-SRAM)是外地可编程门阵列(FPGA)目前内部静态内存的标准。尽管在过去十年中E-SRAM技术有了显著改善,但E-SRAM技术尚未实现超快、节能静态随机内存的目标。然而,对光学静态随机内存(O-SRAM)的初步研究显示,在创造节能超快静态内存(O-SRAM)方面,取得了可喜成果。本文调查了O-SRAM相对于E-SRAM的存取速度和能量性能的优势,同时执行稀有的Tensor Tensor Temri-Khatri-Rao产品(SPMTKRP ) 。 SpMTKRP是数据科学应用中大量使用的高压分解算法的一个基本组成部分。评价结果显示,O-SRAMs可以达到1.1x-2.9x的速度,而与常规的E-SRAM技术相比,可以节省2.8x-8.1x能量。