Nature has inspired a lot of problem solving techniques over the decades. More recently, researchers have increasingly turned to harnessing nature to solve problems directly. Ising machines are a good example and there are numerous research prototypes as well as many design concepts. They can map a family of NP-complete problems and derive competitive solutions at speeds much greater than conventional algorithms and in some cases, at a fraction of the energy cost of a von Neumann computer.However, physical Ising machines are often fixed in its problem solving capacity. Without any support, a bigger problem cannot be solved at all. With a simple divide-and-conquer strategy, it turns out, the advantage of using an Ising machine quickly diminishes. It is therefore desirable for Ising machines to have a scalable architecture where multiple instances can collaborate to solve a bigger problem. We then discuss scalable architecture design issues which lead to a multiprocessor Ising machine architecture. Experimental analyses show that our proposed architectures allow an Ising machine to scale in capacity and maintain its significant performance advantage (about 2200x speedup over a state-of-the-art computational substrate). In the case of communication bandwidth-limited systems, our proposed optimizations in supporting batch mode operation can cut down communication demand by about 4-5x without a significant impact on solution quality. CCS CONCEPTS• Computer systems organization → Analog computers.

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This paper addresses the challenges in accurate and real-time traffic congestion prediction under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction framework that delivers higher accuracy and lower latency than SOTA solutions. While traditional solutions face the dilemma from the trade-off between algorithm complexity and computational efficiency, our Ising-based method breaks away from the trade-off leveraging the Ising model's strong expressivity and the Ising machine's strong computation power. In particular, Ising-Traffic formulates traffic prediction under uncertainty into two Ising models: Reconstruct-Ising and Predict-Ising. Reconstruct-Ising is mapped onto modern Ising machines and handles uncertainty in traffic accurately with negligible latency and energy consumption, while Predict-Ising is mapped onto traditional processors and predicts future congestion precisely with only at most 1.8% computational demands of existing solutions. Our evaluation shows Ising-Traffic delivers on average 98X speedups and 5% accuracy improvement over SOTA.

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