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Found 5 papers, 3 papers with code
torchmSAT: A GPU-Accelerated Approximation To The Maximum Satisfiability Problem
Then, we present a novel neural network architecture to model our differentiable function, and progressively solve MaxSAT using backpropagation.
Automatic MILP Solver Configuration By Learning Problem Similarities
We show that instances that have similar costs using one solver configuration also have similar costs using another solver configuration in the same runtime environment.
BitTrain: Sparse Bitmap Compression for Memory-Efficient Training on the Edge
However, memory footprint from activations is the main bottleneck for training on the edge.
Characterizing and Optimizing EDA Flows for the Cloud
However, deploying EDA jobs on the cloud requires EDA teams to deeply understand the characteristics of their jobs in cloud environments.
DRiLLS: Deep Reinforcement Learning for Logic Synthesis
Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used.
