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 pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mixed-precision quantization%2C 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%2C and in a hardware-aware manner%2C greatly reducing the time required to generate Pareto-optimal DNNs in terms of accuracy versus cost (i.e.%2C latency or memory). We test our approach on three edge-relevant benchmarks%2C namely CIFAR-10%2C Google Speech Commands%2C and Tiny ImageNet. When targeting the optimization of the memory footprint%2C we are able to achieve a size reduction of 47.50%25 and 69.54%25 at iso-accuracy with the baseline networks with all weights quantized at 8 and 2-bit%2C respectively. Our method surpasses a previous state-of-the-art approach with up to 56.17%25 size reduction at iso-accuracy. With respect to the sequential application of state-of-the-art pruning and mixed-precision optimizations%2C we obtain comparable or superior results%2C but with a significantly lowered training time. In addition%2C we show how well-tailored cost models can improve the cost versus accuracy trade-offs when targeting specific hardware for deployment.?quality=80&w=800)
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