84 citations · 150 across the 8 of their papers we have counts for
16 papers
TinyTurbo: Efficient Turbo Decoders on Edge
S Ashwin Hebbar, Rajesh K Mishra, Sravan Kumar Ankireddy +3
In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much be…
DeepIC: Coding for Interference Channels via Deep Learning
Karl Chahine, Nanyang Ye, Hyeji Kim
The two-user interference channel is a model for multi one-to-one communications, where two transmitters wish to communicate with their corresponding receivers via a shared wireles…
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…