READ-based In-Memory Computing Using Sentential Decision Diagrams

Processing-in-memory (PIM) has the potential to unleash unprecedented computing capabilities. While most in-memory computing paradigms rely on repeatedly programming the non-volatile memory devices, recent computing paradigms are capable of evaluating Boolean functions by simply observing the flow of electrical currents within a crossbar of non-volatile memory. Synthesizing Boolean functions into such crossbar designs is a fundamental problem for next-generation in-memory computing systems. The selection of the data structure used to guide the synthesis process has a first-order impact on the overall system performance. State-of-the-art in-memory computing paradigms leverage representations such as majority inverter graphs (MIGs), and binary decision diagrams (BDDs). In this paper, we propose the Cascading Crossbar Synthesis using SDDs ((CS2)-S-2) framework for automatically synthesizing Boolean logic into crossbar designs. The cornerstone of the (CS2)-S-2 framework is a newly invented data structure called sentential decision diagrams (SDDs). It has been proved that SDDs are more succinct than binary decision diagrams (BDDs). To minimize expensive data transfer on the system bus, (CS2)-S-2 maps computation to multiple crossbars that are connected together in series. The (CS2)-S-2 framework is evaluated using 13 benchmark circuits. Compared with state-of-the-art paradigms such as CONTRA, FLOW, and PATH, (CS2)-S-2 improves energy-efficiency by 6.8x while maintaining similar latency.