296 citations · 1.5k across the 45 of their papers we have counts for
6 papers · 1 filter
Neural networks on microcontrollers: saving memory at inference via operator reordering
Edgar Liberis, Nicholas D. Lane
Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence. Microcontrollers (MCUs) are an attractive platform for build…
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors
Royson Lee, Stylianos I. Venieris, Łukasz Dudziak +2
In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR). SR entails the upscaling of a single low…
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning
Łukasz Dudziak, Mohamed S. Abdelfattah, Ravichander Vipperla +2
End-to-end automatic speech recognition (ASR) models are increasingly large and complex to achieve the best possible accuracy. In this paper, we build an AutoML system that uses re…
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
Mario Almeida, Stefanos Laskaridis, Ilias Leontiadis +2
In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitor…
Learning Bodily and Temporal Attention in Protective Movement Behavior Detection
Chongyang Wang, Min Peng, Temitayo A. Olugbade +3
For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e.g., fear and…
Chronic-Pain Protective Behavior Detection with Deep Learning
Chongyang Wang, Temitayo A. Olugbade, Akhil Mathur +3
In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feare…