Title

Stochastic-Based Spin-Programmable Gate Array With Emerging Mtj Device Technology

Keywords

Emerging devices; FPGA; Stochastic computing

Abstract

This paper describes the stochastic-based Spin-Programmable Gate Array (SPGA), an innovative architecture attempting to exploit the stochastic switching behavior newly found in emerging spintronic devices for reconfigurable computing. While many recently studies have investigated using Spin Transfer Torque Memory (STTM) devices to replace configuration memory in field programmable gate arrays (FPGAs), our study, for the first time, attempts to use the quantum-induced stochastic property exhibited by spintronic devices directly for reconfiguration and logic computation. Specifically, the SPGA was designed from scratch for high performance, routability, and ease-of-use. It supports variable-granularity multiple-input-multiple-output (MIMO) logic blocks and variable-length bypassing interconnects with a symmetrical structure. Due to its unconventional architectural features, the SPGA requires several major modifications to be made in the standard VPR placement/routing CAD flow, which include a new technology mapping algorithm based on computing (k, l)-cut, a new placement algorithm, and a modified delay-based routing procedure.Previous studies have shown that, simply replacing reconfiguration memory bits with spintronic devices, the conventional 2D island-style FPGA architecture can achieve approximately 5 times area savings, 2 times speedup and 1.6 times power savings. Our mixed-mode simulation results have shown that, with FPGA architecture innovations, on average, a SPGA can further achieve more than 10 times improvement in logic density, about 5 times improvement in average net delay, and about 5 times improvement in the critical-path delay for the largest 12 MCNC benchmark circuits over an island-style baseline FPGA with spintronic configuration bits.

Publication Date

9-1-2016

Publication Title

Journal of Low Power Electronics and Applications

Volume

6

Issue

3

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.3390/jlpea6030015

Socpus ID

84986598361 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84986598361

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