Design of a Mixed-Signal Compute-in-Memory Ising Solver With Sub- $\mu$ s Time-to-Solution and Optimal Decaying Noise Profile

Combinatorial and discrete optimization problems are prevalent in fields such as artificial intelligence, supply chain management, and wireless communications. The Ising machine, a quantum-inspired paradigm, offers a novel approach to accelerate these computations. However, realizing an Ising machine in an area/energy-efficient and scalable manner with low compute latency in CMOS is challenging. In this work, we propose a new mixed-signal SRAM-based compute-in-memory architecture to perform as a simulated bifurcation (SB) Ising machine. This realization leverages the inherent noise of analog computing, accelerating the time to anneal by injecting decaying noise in the analog domain. We have verified our solution and studied parameter optimization in the
$\bm{180nm}$
CMOS process with up to 60 spins using a post-layout co-simulation framework based on Synopsys PrimeSim. We benchmarked our design on 60-node random binary MAXCUT problems with all-to-all connections. This architecture achieves
$\bm{+95\%}$
of the ground state consistently over 10 graphs with
$<\bm{1\mu s}$
run time and
$\bm{7.6mW}$
average power. This work further discusses techniques to optimize the injected decaying noise profile and SB tuning parameters, which are both crucial to high accuracy bifurcation. We analyze the performance of our proposed tuning methods over different graph densities, increasing number of nodes, and multi-bit graph edge weights.