IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Early Access, 12 May 2022.
Minh S. Q. Truong, Liting Shen, Alexander Glass, Alison Hoffmann L. Richard Carley, James A. Bain, Saugata Ghose*
Carnegie Mellon University
* University of Illinois, Urbana-Champaign
Modern computing applications based upon machine learning can incur significant data movement overheads in state-of-the-art computers. Resistive-memory-based processingusing- memory (PUM) can mitigate this data movement by instead performing computation in situ (i.e., directly within memory cells), but device-level limitations restrict the practicality and/or performance of many PUM architecture proposals. The RACER architecture overcomes these limitations, by proposing efficient peripheral circuitry and the concept of bit-pipelining to enable high-performance, high-efficiency computation using small memory tiles. In this work, we extend RACER to adapt easily to different PUM logic families, by (1) modifying the device access circuitry to support a wide range of logic families, (2) evaluating three logic families proposed by prior work, and (3) proposing and evaluating a new logic family called OSCAR that significantly relaxes the switching voltage constraints required to perform logic with resistive memory devices. We show that the modified RACER architecture, using the OSCAR logic family, can enable practical PUM on real ReRAM devices while improving performance and energy savings by 30% and 37%, respectively, over the original RACER work.
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