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Recently there have been efforts to solve difficult computation problems harnessing or drawing inspiration from nature. A prominent example is the use of Ising machines for solving NP-complete problems 1, 23. Ising machines have evolved from quantum/optical annealers and oscillator-based designs 1 to the recent CMOS-based Von-Neumann 36/in-memory designs 35. While prior works have demonstrated the power of Ising machines to solve complex real-world problems, the state-of-the-art Ising accelerators are dedicated accelerators that are useful only for a class of problems, involve complex data converter circuits (ADCs/DACs), are unreliable compared to the rest of the CMOS SoC due to the use of process-variation sensitive/specific embedded memory technologies. In this paper, we present an all-digital Ising architecture realized using repurposing of L1 cache of a CPU. It relies on processing in-memory technology implemented in SRAM. SACHI solves the reliability problems of prior works such as BRIM, eliminates the need for ADCs/DACs, and provides Ising compute acceleration with minor hardware overhead over a CPU pipeline. The novelty of the proposed approach consists of (i) tightly coupled interfacing of the accelerator to the CPU, (ii) reuse/ repurposing of existing hardware to provide acceleration, (iii) ability to achieve higher parallelism than earlier Ising designs due to reuse-aware compute, and (iv) improved performance/energy for a wide variety of large-sized high precision real-life optimization problems using novel compute/mapping strategies. In comparison to BRIM, the proposed all-digital Ising accelerator achieves (i) 36x, 160x, 286x, 300x better performance, (ii) 72x, 79x, 80x, and 75x improved energy, (iii) reuse of 4x, 32x, 200x, and 4000x is observed for asset allocation, molecular dynamics, image segmentation, and traveling salesman respectively.
Raman et al. (Sat,) studied this question.