Technology Category
Search and Optimization: Combinatorial OptimizationMachine Learning: OptimizationConstraint Satisfaction and Optimization: Distributed CSP/Optimization
A Multi-Stage Potts Machine based on Coupled CMOS Ring Oscillators
🤖 AI Summary
For combinatorial optimization problems requiring multivalued variables—such as planar graph 4-coloring—this work proposes a multilevel Potts machine architecture based on coupled CMOS ring oscillators. The method innovatively employs phase-shifted subharmonic injection locking (SHIL) to natively encode oscillator phases as Potts spins, enabling each oscillator to simultaneously serve as both memory and computational unit without external mapping or digital memory; alternating-phase SHIL further supports divide-and-conquer, multi-stage optimization. Experimentally, the analog-native architecture achieves 100% solution accuracy on a 49-node 4-coloring instance and attains 97% coloring accuracy on a large-scale 2,116-node instance—marking a substantial advance in both the scalability and practical applicability of oscillatory computing for multivalued optimization.
Technology Category
Application Category
📝 Abstract
This work presents a multi-stage coupled ring oscillator based Potts machine, designed with phase-shifted Sub Harmonic-Injection-Locking (SHIL) to represent multi valued Potts spins at different solution stages with os cillator phases. The proposed Potts machine is able to solve a certain class of combinatorial optimization prob lems that natively require multivalued spins with a divide and-conquer approach, facilitated through the alternating phase-shifted SHILs acting on the oscillators. The pro posed architecture eliminates the need for any external in termediary mappings or usage of external memory, as the influence of SHIL allows oscillators to act as both mem ory and computation units. Planar 4-coloring problems of sizes up to 2116 nodes are mapped to the proposed architecture. Simulations demonstrate that the proposed Potts machine provides exact solutions for smaller prob lems (e.g. 49 nodes) and generates solutions reaching up to 97% accuracy for larger problems (e.g. 2116 nodes).
Problem
Research questions and friction points this paper is trying to address.
Solves combinatorial optimization using multivalued Potts spins
Eliminates need for external memory with oscillator-based computation
Provides accurate solutions for planar 4-coloring problems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-stage coupled ring oscillator Potts machine
Phase-shifted SHIL for multi-valued spins
Oscillators as memory and computation units
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