Multiply-Accumulate Enhanced BDD-based Logic Synthesis on RRAM Crossbars

Resistive random access memory (RRAM) is a nonvolatile memory technology which allows to perform computations in both digital and analog circuits. Multiply-Accumulate (MAC) is an analog column-based operation enabled on RRAM crossbars providing high efficiency to perform complex matrix vector multiplications, which is attractive for neural network accelerators. However, the analog computational capability of RRAM devices has not been yet utilized for logic synthesis. In this paper, we show how a synthesis approach based on binary decision diagrams (BDD) can efficiently exploit efficient MAC computation enabled by RRAM. The proposed approach highly benefits from a symmetric structure of Boolean functions. Therefore, a design methodology is presented which optimizes and approximates BDDs under provided error thresholds to maximize efficiency of synthesized logic circuits under negligible loss of accuracy. In the experiments, we show that our proposed synthesis approach allows for an average reduction of up to 47% in the number of operations and up to 66% in the number of required devices compared to state-of-the art methods, even without approximation. Using approximation, we can further reduce the number of required devices.