An FPGA-ASIC Co-Design Framework for Capacity-Constrained Physics-Based Ising Chips

Abstract

When a problem exceeds an analog Ising machine's spin capacity it cannot be solved in one shot: a digital orchestration layer must iteratively decompose the graph, clamp boundary spins, and deliver hardware-sized subproblems to the solver. As per-subproblem solve times reach the us-scale regime, this digital layer (not the analog core) becomes the primary scalability bottleneck. On a 28 nm CMOS coupled-oscillator Ising chip (T_core ~ 77.5 us, 24 mW), a CPU orchestrator leaves the solver idle for over 98% of every iteration at N=750, and the gap survives OpenMP and AVX2 optimization because it is dominated by data-dependent memory access rather than arithmetic throughput. We argue that the orchestration layer is a first-class design object and propose a sizing methodology for hybrid analog-digital Ising systems: given a solver's core time, clock frequency, and target problem class, it gives an analytical baseline for hardware parallelism and memory-bandwidth dimensioning. Guided by the sizing laws, an FPGA orchestration layer co-located with the chip satisfies the pipeline condition with substantial margin. Across graph coloring, MaxCut, and SAT benchmarks it delivers 9.8x-46.5x raw end-to-end time-to-solution (TTS) improvements (14.8x-65.9x on per-repeat runtime) over the optimized CPU orchestrator, with device-level power reductions of 85x-116x.