论文标题
重新升级：RERAM中的低成本浮点处理，以加速迭代线性求解器
ReFloat: Low-Cost Floating-Point Processing in ReRAM for Accelerating Iterative Linear Solvers
论文作者
论文摘要
电阻随机访问存储器（RERAM）是一种有前途的技术，可以在模拟域中执行低成本和原位矩阵矢量乘法（MVM）。科学计算需要高精度浮点（FP）处理。但是，由于较大的FP值范围，RERAM中执行浮点数计算是具有挑战性的。在这项工作中，我们介绍了Refloat，一种数据格式和一种加速器体系结构，用于重新启动的低成本和高性能浮点处理，用于迭代线性求解器。 REFLOAT匹配RERAM横式硬件，并代表一个fp值的块，其位降低了，并且对于高范围的动态表示形式而言，较小的指数基础。因此，反复分析可减少重新兰氏横杆的消耗量，而加工周期更少，并克服了先前工作中的非跨性问题。与最新的基于重新兰异的加速器相比，对套件矩阵的评估表明，求解器时间的重新升级为5.02倍至84.28倍。
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.
