A co-evolutionary method evolves LLM prompts and circuits to produce 8-bit approximate multipliers with better error-area trade-offs than EvoApproxLib.
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MileStone models compiler phase ordering as a multi-objective optimization problem using graph representations, GNN predictions, and RL agents to find Pareto-optimal pass sequences under user constraints.
Graph contraction reduces TSP instances to smaller sub-problems solvable by quantum annealers, shown via Path Integral Monte Carlo simulation and D-Wave hardware.
citing papers explorer
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Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation
A co-evolutionary method evolves LLM prompts and circuits to produce 8-bit approximate multipliers with better error-area trade-offs than EvoApproxLib.
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MileStone: A Multi-Objective Compiler Phase Ordering Framework for Graph-based IR-Level Optimization
MileStone models compiler phase ordering as a multi-objective optimization problem using graph representations, GNN predictions, and RL agents to find Pareto-optimal pass sequences under user constraints.
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A Hybrid Classical-Quantum Annealing Algorithm for the TSP
Graph contraction reduces TSP instances to smaller sub-problems solvable by quantum annealers, shown via Path Integral Monte Carlo simulation and D-Wave hardware.