Low-Cost Floating-Point Processing in ReRAM for Scientific Computing

We propose ReFloat, a data format and an accelerator architecture, for low-cost and high-performance floating-point processing in ReRAM for scientific computing. ReFloat reduces bit number for floating-point representation and processing to achieve a smaller number ofReRAM crossbars and processing cycles for floating-point matrix-vector multiplication. In the ReFloat data format, for scalars of a matrix block, an exponent offset is derived from an optimized base, and then an exponent offset is re-served for each scalar and fraction bit numbers are reduced. The exponent base optimization is enabled by taking advantage of the existence of value locality in real-world matrices. After defining the ReFloat data format, we develop the conversion scheme from default double-precision floating-point format to ReFloat format, the computation procedure, and the low-cost high-performance floating-point processing architecture in ReRAM. With ReFloat, we find that for all 12matrices only 3 bits for matrix exponent, matrix fraction and vector exponent, and 8 or 16 bits for vector fraction are sufficient to ensure convergence on solvers CG and BiCGSTAB.It translates to an average speedup of20.10x/24.59x onCG / BiCGSTAB compared with a GPU baseline and an average speedup of18.86x/54.00x on CG / BiCGSTAB compared with a state-of-the-art ReRAM-based accelerator for scientific computing.