DP-MAP: Towards Resistive Dot-Product Engines with Improved Precision

The natural multiply and accumulate feature of memristor crossbar arrays promises unprecedented processing capabilities to resistive dot-product engines (DPEs), which can accelerate approximate matrix-vector multiplication. To overcome the challenges of low-precision devices and voltage drop over non-zero array parasitics, each matrix element can be represented using two memristors. In this paper, we propose differential pair map (DP-MAP) - the first matrix to memristor conductance mapping algorithm specifically designed for crossbars with a differential pair configuration. In contrast, previous works consider the differential pair configuration as an afterthought, which limits the achievable precision. The specified conductance values are next programmed to the memristor hardware using accurate closed-loop tuning. Analog computation with high precision is attained by judiciously selecting the conductance range and avoiding to explicitly decompose each matrix into a positive and negative component. Short run-time is achieved using a hierarchical optimization algorithm and two speed-up techniques. Compared with earlier studies, the computational accuracy is improved with 3.36X. This translates into signal and image compression with 61 % and 94% higher quality, respectively. The simulation time of complex physical systems modeled using partial differential equations (PDEs) is reduced with 5.87X.