3 accepted papers
Reducing the high computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments. We first show the greedy approach of recent channel pruning methods ignores the inherent quadratic coupling between channels in the neighboring lay…
Deploying deep convolutional neural networks on ultra-low power systems is challenging due to the extremely limited resources. Especially, the memory becomes a bottleneck as the systems put a hard limit on the size of on-chip memory. Because peak memory explosion in the lower layers is critical even…
Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded acc…