Resistive random access memory (ReRAM) is a promising technology that can perform low-cost and in-situ matrix-vector multiplication (MVM) in analog domain. Scientific computing requires high-precision floating-point (FP) processing. However, performing floating-point computation in ReRAM is challenging because of high hardware cost and execution time due to the large FP value range. In this work we present ReFloat, a data format and an accelerator architecture, for low-cost and high-performance floating-point processing in ReRAM for iterative linear solvers. ReFloat matches the ReRAM crossbar hardware and represents a block of FP values with reduced bits and an optimized exponent base for a high range of dynamic representation. Thus, ReFloat achieves less ReRAM crossbar consumption and fewer processing cycles and overcomes the noncovergence issue in a prior work. The evaluation on the SuiteSparse matrices shows ReFloat achieves 5.02x to 84.28x improvement in terms of solver time compared to a state-of-the-art ReRAM based accelerator.

翻译：电阻式随机存取存储器（ReRAM）作为一种新兴技术，可在模拟域内实现低成本的存内矩阵向量乘法（MVM）。科学计算对高精度浮点（FP）处理有刚性需求，然而在ReRAM中执行浮点运算面临巨大挑战：由于浮点数值范围宽泛，导致硬件成本高昂且执行时间冗长。本文提出ReFloat数据格式与加速器架构，旨在为迭代线性求解器实现低成本、高性能的ReRAM浮点处理。ReFloat通过适配ReRAM交叉阵列硬件，采用缩位表示的浮点数值块与优化的指数基，实现高动态表示范围，从而降低ReRAM交叉阵列消耗与处理周期数，并克服了现有工作中的非收敛问题。基于SuiteSparse矩阵集的评估表明，与当前最先进的ReRAM加速器相比，ReFloat在求解时间上实现了5.02倍至84.28倍的性能提升。