  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.
