Analogue memory hardware powers bio-inspired probabilistic decision making
Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1
Artificial IntelligenceMachine Learning
Summary
Animals make decisions by combining what they sense with what they already believe, even when unsure. The authors explain how brain-like random activity in neurons and connections can model this by exploring many possible states to learn and decide. They found that certain noisy electrical memory chips can mimic this process efficiently in computers. This could help build machines that learn and make decisions in a way similar to the brain, handling uncertainty naturally.
What this means in practice
- •For hardware engineers: Develop energy-efficient computing chips that exploit intrinsic electrical noise for probabilistic inference tasks.$Commercial implications: This paper enables creation of analogue memory-based chips capable of brain-like uncertain decision-making for AI hardware markets.
- •For embedded system developers: Implement bio-inspired algorithms for uncertainty-aware learning directly on low-power memory devices suitable for edge computing.
A position paper. It proposes an approach and reports no results.
Authors
Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado, Devendra Vyas, Tommaso Salvatori
Abstract
Learning and decision-making in animals are often modeled as Bayesian processes, where sensory evidence is integrated with prior beliefs to guide behavior in the face of uncertainty. But what are the inherent neural dynamics that give rise to this ability, and how could they be replicated in computing systems? This abstract discusses a biologically grounded framework in which noisy neural and synaptic dynamics perform inference and learning via stochastic sampling from an internal energy function, capturing uncertainty over latent states and model parameters through neural and synaptic variability, respectively. This enables approaches such as predictive coding networks to account for epistemic uncertainty via Markov chain Monte Carlo sampling. Drawing a parallel between intrinsic noise in biological systems and electrical noise in emerging probabilistic analogue memory technologies, we highlight how analogue in-memory computing hardware naturally emerges as the solution for massively scalable and energy-efficient probabilistic inference.