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）是一种有前途的技术，可在模拟域中执行低成本且原位的矩阵-向量乘法（MVM）。科学计算需要高精度浮点（FP）处理。然而，由于浮点数值范围较大导致的硬件成本高昂和执行时间较长，在ReRAM中实现浮点计算极具挑战性。本文提出ReFloat——一种数据格式与加速器架构，旨在实现ReRAM中用于迭代线性求解器的低成本高性能浮点处理。ReFloat适配ReRAM交叉阵列硬件，通过缩减比特数并采用优化指数基对浮点值块进行表示，从而获得高动态表示范围。因此，ReFloat减少了ReRAM交叉阵列消耗和处理周期数，并克服了先前工作中的非收敛问题。基于SuiteSparse矩阵的评估表明，与当前最先进的基于ReRAM的加速器相比，ReFloat在求解时间上实现了5.02倍至84.28倍的提升。