SCRIMP: a general s tochastic c omputing architecture using R eRAM i n- m emory p rocessing

Stochastic computing (SC) reduces the complexity of computation by representing numbers with long independent bit-streams. However, increasing performance in SC comes with increase in area and loss in accuracy. Processing in memory (PIM) with non-volatile memories (NVMs) computes data inplace, while having high memory density and supporting bitparallel operations with low energy. In this paper, we propose SCRIMP for s tochastic c omputing acceleration with r esistive RAM (ReRAM) i n- m emory processing, which enables SC in memory. SCRIMP can be used for a wide range of applications. It supports all SC encodings and operations in memory. It maximizes the performance and energy efficiency of implementing SC by introducing novel in-memory parallel stochastic number generation and efficient implication-based logic in memory. To show the efficiency of our stochastic architecture, we implement image processing on the proposed hardware.