Error-Resilient Spintronics via the Shannon- Inspired Model of Computation

Abstract

The energy and delay reductions from CMOS scaling have stagnated, motivating the search for a CMOS replacement. Spintronic devices are one of the promising beyond-CMOS alternatives. However, they exhibit high switching error rates of 1% or more when operated at energy and delay comparable to CMOS, rendering them incompatible with the deterministic nature of digital implementations. In this paper, we employ a Shannon-inspired model of computation to enhance the tolerance of all-spin logic (ASL)-based implementations to gate-level switching errors. We develop the logic-level path delay reallocation techniques to shape the output error statistics and propose a novel error compensation scheme to achieve 1000× higher tolerance to device-level switching errors while maintaining the classification accuracy of an ASL-based support vector machine (SVM) classifier.

Keywords

• All spin logic
• beyond-CMOS
• machine learning
• spintronics
• statistical computing