Input-Aware Flow-Based In-Memory Computing

In-memory computing using nanoscale crossbar arrays is a promising solution strategy to overcome the limitations of the von Neumann architecture. Flow-based computing is an emerging in-memory computing paradigm for evaluating Boolean logic using the natural flow of electrical currents. Previous studies on flow-based computing have focused on synthesizing crossbar designs with small dimensions to improve various performance metrics. In this paper, we observe that the latency and energy of evaluating a Boolean input vector is dependent on the state of the crossbar design (or the previous input vector). To take advantage of this observation, we propose the REORDER framework that reorders the sequence of input vectors to improve performance. The reordering reduces the overall number of WRITE operations to the non-volatile memory devices, which has a first-order impact on the overall performance of flow-based computing systems. The optimal input sequence can be obtained by formulating and solving a traveling salesman problem (TSP). The REORDER framework leverages a heuristic solution to balance pre-processing overhead with reduction in device switching. We evaluate the REORDER framework on image processing applications that allow input vector reordering. Compared with a naive input sequence, the framework improves time and energy efficiency by 78% and 69% respectively for image filtering and by 94% and 72% respectively for feature extraction.