20 citations · 52 across the 6 of their papers we have counts for
6 papers
DISPATCH: Design Space Exploration of Cyber-Physical Systems
Prerit Terway, Kenza Hamidouche, Niraj K. Jha
Design of cyber-physical systems (CPSs) is a challenging task that involves searching over a large search space of various CPS configurations and possible values of components comp…
Fully Dynamic Inference with Deep Neural Networks
Wenhan Xia, Hongxu Yin, Xiaoliang Dai +1
Modern deep neural networks are powerful and widely applicable models that extract task-relevant information through multi-level abstraction. Their cross-domain success, however, i…
STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods
Shayan Hassantabar, Xiaoliang Dai, Niraj K. Jha
Neural networks (NNs) have been successfully deployed in many applications. However, architectural design of these models is still a challenging problem. Moreover, neural networks…
DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks
Hongxu Yin, Bilal Mukadam, Xiaoliang Dai +1
Diabetes impacts the quality of life of millions of people. However, diabetes diagnosis is still an arduous process, given that the disease develops and gets treated outside the cl…
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM
Hongxu Yin, Guoyang Chen, Yingmin Li +3
Many long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promisin…
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
Xiaoliang Dai, Peizhao Zhang, Bichen Wu +10
This paper proposes an efficient neural network (NN) architecture design methodology called Chameleon that honors given resource constraints. Instead of developing new building blo…