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Found 3 papers, 1 papers with code
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization
In this work, we present a flexible and scalable framework for automated mixed-precision quantization that concurrently optimizes task performance, memory compression, and compute savings through multi-objective evolutionary computing.
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation.
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it.
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