Distributed Injection-Locking in Analog Ising Machines to Solve Combinatorial Optimizations
The oscillator-based Ising machine (OIM) is a network of coupled CMOS oscillators that solves combinatorial optimization problems. In this paper, the distribution of the injection-locking oscillations throughout the circuit is proposed to accelerate the phase-locking of the OIM. The implications of the proposed technique theoretically investigated and verified by extensive simulations in EDA tools with a $130~nm$ PTM model. By distributing the injective signal of the super-harmonic oscillator, the speed is increased by $219.8\%$ with negligible increase in the power dissipation and phase-locking error of the device due to the distributed technique.
Code (0)
등록된 구현이 없습니다.
Tasks
Combinatorial OptimizationSimilar Papers 제목 키워드 기반
Enhanced Polarization Locking in VCSELs
While optical injection locking (OIL) of vertical-cavity surface-emitting lasers (VCSELs) has been widely studied in the past, the polarization dynamics of OIL have received far less attention. Recent studies suggest tha…
Beyond Gradient Descent: Adam for Analog Ising Machines
As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems. However, many analog, time-continuous Ising machines rely on gradient-descent-like d…
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning
Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization. However, while various neural networks can be implemented with Ising machines, their inabil…
BIG-bench Machine LearningCombinatorial OptimizationAnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI
Recent advances in generative AI (GenAI) have shown transformative potential for modern hardware design. However, existing GenAI-driven approaches fall short of enabling large-scale electronic design automation (EDA) due…
Federated LearningReducing hyperparameter sensitivity in measurement-feedback based Ising machines
Analog Ising machines have been proposed as heuristic hardware solvers for combinatorial optimization problems, with the potential to outperform conventional approaches, provided that their hyperparameters are carefully …