Synthesis Of Normally-Off Boolean Circuits: An Evolutionary Optimization Approach Utilizing Spintronic Devices

Keywords

Evolutionary algorithm; Magnetic Tunnel Junction (MTJ); Majority logic; Normally-off computing (NoC); spin Hall Effect (SHE); Spintronic devices

Abstract

In this paper, we develop an evolutionary-driven circuit optimization methodology, which can be leveraged for the synthesis of spintronic-based normally-off computing (NoC) circuits. NoC architectures distribute nonvolatile memory elements throughout the CMOS logic plane, creating a new class of fine-grained functionally-constrained synthesis challenges. Spin-based NoC circuits synthesis objectives include increased computational throughput and reduced static power consumption. Our proposed methodology utilizes Genetic Algorithms (GAs) to optimize the implementation of a Boolean logic expression in terms of area, delay, or power consumption. It first leverages the spin-based device characteristics to achieve a primary semi-optimized implementation, then further performance optimization is applied to the implemented design based on the NoC requirements and optimization criteria. As a proof-of-concept, the optimization approach is leveraged to implement a functionally-complete set of Boolean logic gates using spin Hall effect (SHE)-magnetic tunnel junctions (MTJs), which are optimized for both power and delay objectives. NoC synthesis methodologies supporting NoC circuit design of emerging device and hybrid CMOS logic applications. Finally, Simulation results and analyses verified the functionality of our proposed optimization tool for NoC circuit implementations.

Publication Date

5-9-2018

Publication Title

Proceedings - International Symposium on Quality Electronic Design, ISQED

Volume

2018-March

Number of Pages

49-54

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ISQED.2018.8357264

Socpus ID

85047943182 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85047943182

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