ReFloat: Low-Cost Floating-Point Processing in ReRAM for Accelerating Iterative Linear Solvers

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Abstract
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 5.02× to 84.28× improvement in terms of solver time compared to a state-of-the-art ReRAM based accelerator.
Funding Information
  • NSF (National Science Foundation) (EPMD-1955246, CNS-2112562)
  • Army Research Office (W911NF-19-2-0107)

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