Tommaso Salvatori

Recursive message passing achieves 99% Sudoku-Extreme accuracy with just 16k parameters, outperforming larger neural baselines on N-queens and SAT with zero-shot generalization.

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.

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 memory.