  The resource requirements of deep neural networks (DNNs) pose significant
challenges to their deployment on edge devices. Common approaches to address
this issue are pruning and mixed-precision quantization, which lead to latency
and memory occupation improvements. These optimization techniques are usually
applied independently. We propose a novel methodology to apply them jointly via
a lightweight gradient-based search, and in a hardware-aware manner, greatly
reducing the time required to generate Pareto-optimal DNNs in terms of accuracy
versus cost (i.e., latency or memory). We test our approach on three
edge-relevant benchmarks, namely CIFAR-10, Google Speech Commands, and Tiny
ImageNet. When targeting the optimization of the memory footprint, we are able
to achieve a size reduction of 47.50% and 69.54% at iso-accuracy with the
baseline networks with all weights quantized at 8 and 2-bit, respectively. Our
method surpasses a previous state-of-the-art approach with up to 56.17% size
reduction at iso-accuracy. With respect to the sequential application of
state-of-the-art pruning and mixed-precision optimizations, we obtain
comparable or superior results, but with a significantly lowered training time.
In addition, we show how well-tailored cost models can improve the cost versus
accuracy trade-offs when targeting specific hardware for deployment.
