A scalable and resource-efficient pipelined p-computer for probabilistic Ising machines

Abstract

Probabilistic Ising machines (PIMs) based on probabilistic bits offer a hardware-friendly route to solve combinatorial optimization problems, but most digital implementations achieve high throughput by exploiting sparse interactions. This limits their applicability to dense problems, for which memory bandwidth and data movement become the dominant bottlenecks. Here, we show a resource-efficient pipelined Field-Programmable Gate Array architecture enabling high-throughput execution of fully-connected PIMs while maintaining scalability and modularity. This architecture design combines a deeply pipelined (>20 stages) probabilistic bit update path, which overlaps spin evaluation and local-field updates, with a bandwidth-aware on-chip memory organization for the coupling and bias matrices. The architecture supports 512 p-bits with 16-bit fixed-point coefficients and 1024 and 2048 p-bits with 10-bit and 2-bit coefficients, respectively, and operates at up to 300 MHz. At fixed degree of parallelization, it delivers an order-of-magnitude higher update rate than an optimized non-pipelined baseline, while improving the time-area trade-off for dense workloads. Validation on portfolio optimization and low-density parity-check decoding shows close agreement with software references and substantial reductions in time-to-solution relative to the non-pipelined design, establishing pipelining as an effective route to scalable digital probabilistic computing for dense optimization problems.