101 citations · 301 across the 44 of their papers we have counts for
5 papers · 1 filter
Deepcode and Modulo-SK are Designed for Different Settings
Hyeji Kim, Yihan Jiang, Sreeram Kannan +2
We respond to [1] which claimed that "Modulo-SK scheme outperforms Deepcode [2]". We demonstrate that this statement is not true: the two schemes are designed and evaluated for ent…
HAPI: Hardware-Aware Progressive Inference
Stefanos Laskaridis, Stylianos I. Venieris, Hyeji Kim +1
Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks. Despite their popularity, CNN inference still comes at a high computation…
Journey Towards Tiny Perceptual Super-Resolution
Royson Lee, Łukasz Dudziak, Mohamed Abdelfattah +4
Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks.…
BRP-NAS: Prediction-based NAS using GCNs
Łukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah +3
Neural architecture search (NAS) enables researchers to automatically explore broad design spaces in order to improve efficiency of neural networks. This efficiency is especially i…
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator
Mohamed S. Abdelfattah, Łukasz Dudziak, Thomas Chau +3
Neural architecture search (NAS) has been very successful at outperforming human-designed convolutional neural networks (CNN) in accuracy, and when hardware information is present,…