Peripheral Circuitry Assisted Mapping Framework for Resistive Logic-In-Memory Computing

TitlePeripheral Circuitry Assisted Mapping Framework for Resistive Logic-In-Memory Computing
Publication TypeConference Paper
Year of Publication2021
AuthorsS Zhang, H Li, and U Schlichtmann
Conference NameIeee/Acm International Conference on Computer Aided Design, Digest of Technical Papers, Iccad
Date Published01/2021

In-memory computing has been applied in different fields due to its superior speed and energy efficiency. Among a variety of memory technologies that have been explored, resistive memory has widely been adopted for various purposes, including Processing-In-Memory (PIM) for neural networks and Logic-In-Memory (LIM) for general logic operations. PIM has intensively been studied in recent years, while the progress in developing LIM computing falls behind. LIM computing is usually implemented based on MAGIC operations, which require inputs to be aligned regularly along rows or columns in a memory crossbar. As the intermediate data generated during the logic execution are normally scattered across the memory crossbar, alignment operations are inserted to align the data, which often costs numerous cycles and dominates the overall latency. In current MAGIC-based designs, alignment operations induce a significant overhead in either area or latency. Therefore, the Area-Latency-Product (ALP), known as a key metric for circuit performance, still has significant optimization potential in LIM computing. In this work, we leverage peripheral circuitry to conduct alignment operations and propose a novel mapping framework to optimize the latency and area costs. Intermediate data are read out, processed in peripheral circuits, then in parallel written back into target cells of the memory crossbar. The approach eliminates the use of redundant memory cells, leading to area reduction. Moreover, it enables simultaneous alignments of multiple intermediate data, which can decrease the overall latency significantly. Based on simulation results, our proposed mapping framework can achieve around 93% ALP reductions on average compared with prior designs with merely 2.13% total area overhead.