Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks
Djohan Bonnet, Tifenn Hirtzlin, Atreya Majumdar, Thomas Dalgaty, Eduardo Esmanhotto, V. Meli, N. Castellani, Simon Martin, Jean-François Nodin, G. Bourgeois, Jean‐Michel Portal, Damien Querlioz, Elisa Vianello
Abstract
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors' inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a "technological loss", incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task.