System and method of using neuroevolution-enhanced multi-objective optimization for mixed-precision quantization of deep neural networks

One or more non-transitory computer-readable media storing instructions executable to perform operations for optimizing multiple objectives of mixed-precision quantization, the operations comprising:

• generating a plurality of graph neural networks (GNNs);
• generating a plurality of new GNNs based on the plurality of GNNs;
• generating a sequential graph for a first DNN, the first DNN comprising a sequence of quantizable operations, each of which includes quantizable parameters and is represented by a different node in the sequential graph;
• inputting the sequential graph into the plurality of GNNs and the plurality of new GNNs;
• evaluating outputs of the plurality of GNNs and the plurality of new GNNs based on conflicting objectives of reducing precisions of the quantizable parameters of the first DNN;
• and
• selecting a GNN from the plurality of GNNs and the plurality of new GNNs based on the evaluation, the GNN to be used for reducing precisions of quantizable parameters of a second DNN.

An apparatus for optimizing multiple objectives of mixed-precision quantization, the apparatus comprising:

• a computer processor for executing computer program instructions;
• and
• a non-transitory computer-readable memory storing computer program instructions executable by the computer processor to perform operations comprising:
• generating a plurality of graph neural networks (GNNs),
• generating a plurality of new GNNs based on the plurality of GNNs,
• generating a sequential graph for a first DNN, the first DNN comprising a sequence of quantizable operations, each of which includes quantizable parameters and is represented by a different node in the sequential graph,
• inputting the sequential graph into the plurality of GNNs and the plurality of new GNNs,
• evaluating outputs of the plurality of GNNs and the plurality of new GNNs based on conflicting objectives of reducing precisions of the quantizable parameters of the first DNN, and
• selecting a GNN from the plurality of GNNs and the plurality of new GNNs based on the evaluation, the GNN to be used for reducing precisions of quantizable parameters of a second DNN.