Hardware Acceleration of Stochastic Neural Networks Enabled by Bit‐Cell Level Co‐Design of Magnetic Tunnel Junctions

ABSTRACT Processing‐in‐memory architectures mitigate data shuttling bottlenecks in deterministic AI workloads, but still depend on costly external entropy sources for probabilistic models. Here, we present a magnetic‐tunnel‐junction‐based processing‐in‐memory architecture that unifies data storage and massively parallel probabilistic computation for stochastic and deterministic neural networks. By introducing bit‐paired stochastic and stable magnetic tunnel junctions, we realize a multi‐precision stochastic memory cell that transduces stored binary weights into variance‐tunable stochastic bitstreams in situ. We evaluate the architecture's versatility across three progressive regimes of stochastic neural computation. First, by using a hardware‐in‐the‐loop prototype interfacing fabricated stochastic spin‐orbit torque magnetic tunnel junctions with a field‐programmable gate array chip, we validate stochastic matrix‐vector multiplications and achieve near‐floating‐point accuracy without specialized retraining. Second, for networks with stochastic neurons, we demonstrate an Ising machine that utilizes adaptive‐precision sampling and dynamic graph routing to reduce time‐to‐solution. Third, we propose an in‐memory Bayesian computer with independent control of weight means and uncertainties, and apply it to a localization problem under unreliable inputs. By keeping intermediate signals in the stochastic domain, the architecture reduces peripheral overhead associated with random‐number generation and data conversion. This device‐circuit co‐design provides a shared hardware platform for stochastic neural inference, combinatorial optimization, and uncertainty‐aware computing.