C.A. Micchelli, W.L. Miranker
Journal of the ACM
With the ever-growing demand for deep learning (DL) at the edge, building small and efficient DL architectures has become a significant challenge. Optimization techniques such as quantization, pruning or hardware-aware neural architecture search (HW-NAS) have been proposed. In this paper, we present an efficient HW-NAS; Compression-Aware Neural Architecture search (CaW-NAS), that combines the search for the architecture and its quantization policy. While former works search over a fully quantized search space, we define our search space with quantized and non-quantized architectures. Our search strategy finds the best trade-off between accuracy and latency according to the target hardware. Experimental results on a mobile platform show that, our method allows to obtain more efficient networks in terms of accuracy, execution time and energy consumption when compared to the state of the art.
C.A. Micchelli, W.L. Miranker
Journal of the ACM
Saurabh Paul, Christos Boutsidis, et al.
JMLR
Joxan Jaffar
Journal of the ACM
Kenneth L. Clarkson, Elad Hazan, et al.
Journal of the ACM