Energy-efficient on-chip learning for a fully connected neural network using domain wall device

Anubha Sehgal; Seema Dhull; Sourajeet Roy; Brajesh Kumar Kaushik

doi:10.1117/12.2648643

15 March 2023 Energy-efficient on-chip learning for a fully connected neural network using domain wall device

Anubha Sehgal, Seema Dhull, Sourajeet Roy, Brajesh Kumar Kaushik

Proceedings Volume 12438, AI and Optical Data Sciences IV; 1243805 (2023) https://doi.org/10.1117/12.2648643
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Spintronic devices have received lots of attention recently due to their potential to provide a solution for the presentday challenge of increased power dissipation. Among spintronic devices, domain-wall synaptic devices are speed and energy efficient for solving image classification, speech recognition, and other problems. In this paper, a fully connected neural network (FCNN) is implemented using energy-efficient domain wall-based synaptic devices and transistor-based feedback circuits. The designed FCNN is trained on-chip for the classification of Fisher's Iris dataset. The proposed neural network achieves an accuracy of 95%. The proposed FCNN is 96% and 83.3% efficient in terms of energy and latency respectively when compared to previously proposed hardware for on-chip learning

Conference Presentation

Citation Download Citation

Anubha Sehgal, Seema Dhull, Sourajeet Roy, and Brajesh Kumar Kaushik "Energy-efficient on-chip learning for a fully connected neural network using domain wall device", Proc. SPIE 12438, AI and Optical Data Sciences IV, 1243805 (15 March 2023); https://doi.org/10.1117/12.2648643

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