This work presents a CUDA-accelerated methodology for training multiple neural networks in parallel using population-based metaheuristics. The goal is to obtain fast and accurate short-term energy-forecasting models for time-sensitive building-management applications. We evaluate…
We propose SPECS, a genetic algorithm for automated analog circuit synthesis with joint topology and sizing optimization. SPECS is inspired by NeuroEvolution of Augmenting Topologies (NEAT), an evolutionary algorithm originally developed to synthesize neural networks. By reformul…
In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing. Traditional methods lack generalization across different performance targets, while standard RL approaches waste resources exploring unpromising regions.…