Optimizing Boolean embedding matrix for compressive sensing in RRAM crossbar

Abstract

The emerging resistive random-access-memory (RRAM) crossbar provides an intrinsic fabric for matrix-vector multiplication, which can be leveraged as power efficient linear embedding hardware for data analytics such as compressive sensing. As the matrix elements are represented by resistance of RRAM cells, it imposes constraints for the embedding matrix due to limited RRAM programming resolution. A random Boolean embedding can be efficiently mapped to the RRAM crossbar but suffers from poor performance. Learning-based embedding matrices can deliver optimized performance but are continuous-valued which prevents it from being mapped to RRAM crossbar structure directly. In this paper, we have proposed one algorithm that can find an optimal Boolean embedding matrix for a given learned real-valued embedding matrix, so that it can be effectively mapped to the RRAM crossbar structure while high performance is preserved. The numerical experiments demonstrate that the proposed optimized Boolean embedding can reduce the embedding distortion by 2.7x, and image recovery error by 2.5x compared to the random Boolean embedding, both mapped on RRAM crossbar. In addition, optimized Boolean embedding on RRAM crossbar exhibits 10x faster speed, 17x better energy efficiency, and three orders of magnitude smaller area with slight accuracy penalty, when compared to the optimized real-valued embedding on CMOS ASIC platform.