A Unified Performance-Cost Landscape of Parallel p-bit Ising Machines Based on Update Dynamics

TL;DR

This paper analyzes the performance and hardware cost trade-offs of synchronous and asynchronous parallel p-bit Ising machines, proposing a time-multiplexed p-bit reuse method to optimize scalability and solution quality.

Contribution

It introduces a structured synchronous control scheme with p-bit reuse, enabling scalable, cost-effective Ising machine architectures that maintain solution quality under realistic hardware constraints.

Findings

Synchronous updates can oscillate under high simultaneity but are not inherently unstable.

Asynchronous updates require slower operation due to hardware delay.

Time-multiplexed p-bit reuse reduces hardware requirements while preserving correct annealing dynamics.

Abstract

Parallel p-bit Ising machines are a promising platform for fast and energy-efficient combinatorial optimization, but their scalability depends on update synchronization, hardware delay, and architectural cost. In this work, we establish a unified performance-cost framework by analyzing synchronous and asynchronous update schemes under realistic constraints, including finite delay, time-multiplexed p-bit reuse, and limited DAC precision. We show that synchronous updates are not inherently unstable but can exhibit oscillations under excessive simultaneity, while asynchronous updates require slower operation due to hardware delay. To address this trade-off, we introduce time-multiplexed p-bit reuse with structured synchronous control, preserving correct annealing dynamics while reducing hardware requirements. This approach decouples statistical correctness from physical resources, enabling the number of p-bits and DACs to scale inversely with the reuse factor. As a result, synchronous architectures achieve comparable or better solution quality at less than half the hardware cost of optimized asynchronous designs on G-set MaxCut benchmarks (800-2000 nodes). We also show that low-resolution DACs (3-4 bits) are sufficient to reach near-optimal solutions when annealing time is properly adjusted. These findings provide practical design guidelines for scalable probabilistic computing hardware under realistic constraints.