A fully parallel densely connected probabilistic Ising machine with inertia for real-time applications

2026-04-18

AI Summary

This work addresses the inefficiency of conventional probabilistic Ising machines in solving densely connected optimization problems due to their inherently serial update mechanisms. The authors propose a novel p-bit dynamics model augmented with an inertia term, enabling fully parallel and synchronous updates across all-to-all connected spins for the first time. Through algorithm-hardware co-design, they implement a parallel Ising machine architecture on an FPGA. The proposed approach achieves substantial computational acceleration—averaging 35× speedup and reaching up to 150× on individual instances for Max-Cut and Sherrington–Kirkpatrick (SK) models with N=200—while maintaining or even improving solution success rates. This performance meets the stringent latency, throughput, and solution quality requirements of real-time 5G MIMO detection.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Ising machines -- special-purpose hardware for heuristically solving Ising optimization problems -- based on probabilistic bits (p-bits) have been established as a promising alternative to heuristic optimization algorithms run on conventional computers. However, it has -- until now -- been thought that Ising spins that are connected in probabilistic Ising machines cannot be updated in parallel without ruining the machine's solving ability. This has been a major challenge for using probabilistic Ising machines as fast solvers for densely connected problems. Here, we circumvent this by introducing a modified Ising spin dynamics with an added inertia term, and verify in algorithm simulations, FPGA hardware emulation, and FPGA experiments that it enables fully parallel, synchronous updates while improving rather than degrading success probability. We evaluated on various types of abstract (Max-Cut and Sherrington-Kirkpatrick-model) and application-derived (MIMO, wireless detection) dense Ising benchmark instances. Performing fully parallel updates results in a speed advantage that grows faster than linearly with the number of spins, giving rise to large time-to-solution increases for practical problem sizes. For both Max-Cut and the SK-1 model at a problem size of 200, our approach achieved an average speedup of $\approx 35\times$, with the best single-instance speedup reaching $150\times$. As an example of the practical utility of our approach in an application where speed is critical, we further show by co-designing the algorithm dynamics with the hardware implementation -- co-optimizing for solver ability and silicon resource usage -- that probabilistic Ising machines based on our approach satisfy the stringent solution quality and latency/throughput requirements for real-time MIMO detection in modern 5G cellular wireless networks while using a practically reasonable silicon area.

Problem

• probabilistic Ising machine
• parallel update
• dense connectivity
• real-time optimization
• Ising spin dynamics

Innovation

• probabilistic Ising machine
• parallel spin update
• inertia dynamics
• hardware-software co-design
• real-time optimization