Associative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays. Issue 3 (1st September 2022)

• Record Type: Journal Article
• Authors: Prasad, Nitin; Mukim, Prashansa; Madhavan, Advait; Stiles, Mark D
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 3(2022)
• Publication Date: 2022-09-01
• Subjects: associative memories -- Hopfield networks -- spin-torque oscillator -- memristor -- magnetic tunnel junctions -- image reconstruction -- CMOS delay circuits; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac7d05
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: Simulations of complex-valued Hopfield networks based on spin-torque oscillators can recover phase-encoded images. Sequences of memristor-augmented inverters provide tunable delay elements that implement complex weights by phase shifting the oscillatory output of the oscillators. Pseudo-inverse training suffices to store at least 12 images in a set of 192 oscillators, representing 16 × 12 pixel images. The energy required to recover an image depends on the desired error level. For the oscillators and circuitry considered here, 5% root mean square deviations from the ideal image require approximately 5 μ s and consume roughly 130 nJ. Simulations show that the network functions well when the resonant frequency of the oscillators can be tuned to have a fractional spread less than 10 −3, depending on the strength of the feedback.