论文标题
性能建模稀疏MTTKRP使用FPGA上的光学静态随机访问存储器
Performance Modeling Sparse MTTKRP Using Optical Static Random Access Memory on FPGA
论文作者
论文摘要
电静电随机内存（E-SRAM）是现场可编程门阵列（FPGA）中内部静态内存的当前标准。尽管在过去十年中，E-SRAM技术取得了巨大的改进，但E-SRAM技术的超快速，节能静态随机内存的目标尚未实现。但是，对光学静态随机访问记忆（O-SRAM）的初步研究在创建节能超快速静态内存方面显示出令人鼓舞的结果。 本文研究了O-SRAM优于E-SRAM在访问速度和能量性能方面的优势，同时执行稀疏的矩阵张量时间Khatri-Rao产品（SPMTTTKRP）。 SPMTTKRP是张量分解算法的重要组成部分，该算法大量用于数据科学应用程序。评估结果表明，与传统的E -SRAM技术相比，O -SRAM可以达到1.1倍-2.9倍的速度，同时节省2.8倍-8.1倍。
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.
