  Mixed-precision quantization is a powerful tool to enable memory and compute
savings of neural network workloads by deploying different sets of bit-width
precisions on separate compute operations. 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. Our framework centers
on Neuroevolution-Enhanced Multi-Objective Optimization (NEMO), a novel search
method, which combines established search methods with the representational
power of neural networks. Within NEMO, the population is divided into
structurally distinct sub-populations, or species, which jointly create the
Pareto frontier of solutions for the multi-objective problem. At each
generation, species perform separate mutation and crossover operations, and are
re-sized in proportion to the goodness of their contribution to the Pareto
frontier. In our experiments, we define a graph-based representation to
describe the underlying workload, enabling us to deploy graph neural networks
trained by NEMO via neuroevolution, to find Pareto optimal configurations for
MobileNet-V2, ResNet50 and ResNeXt-101-32x8d. Compared to the state-of-the-art,
we achieve competitive results on memory compression and superior results for
compute compression. Further analysis reveals that the graph representation and
the species-based approach employed by NEMO are critical to finding optimal
solutions.
