Author

Eiji Kawasaki

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Preprint Sep 2026

Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 2

This report demonstrates using a detailed transaction-level model (TLM) of a probabilistic analogue in-memory computing (AIMC) processor that the same energy-based model can execute well over 1000x faster than data-center-grade hardware by eliminating the HBM interface and performing computation directly within on-chip...

Thomas Dalgaty, Eiji Kawasaki, M. de Prado et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1

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 is discussed.

Thomas Dalgaty, Eiji Kawasaki, M. de Prado et al. · 1 citation