All-Spin Bayesian Neural Networks

Abstract

Probabilistic machine learning enabled by the Bayesian formulation has recently gained significant attention in the domain of automated reasoning and decision-making. While impressive strides have been made recently to scale up the performance of deep Bayesian neural networks, they have been primarily standalone software efforts without any regard to the underlying hardware implementation. In this paper, we propose an "All-Spin" Bayesian Neural Network where the underlying spintronic hardware provides a better match to the Bayesian computing models. To the best of our knowledge, this is the first exploration of a Bayesian neural hardware accelerator enabled by emerging post-CMOS technologies. We develop an experimentally calibrated device-circuit-algorithm co-simulation framework and demonstrate $24\times$ reduction in energy consumption against an iso-network CMOS baseline implementation.

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• An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM)
• Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
• Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving
• Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks