7 citations · 13 across the 3 of their papers we have counts for
10 papers
CoSA: Scheduling by Constrained Optimization for Spatial Accelerators
Qijing Huang, Minwoo Kang, Grace Dinh +5
Recent advances in Deep Neural Networks (DNNs) have led to active development of specialized DNN accelerators, many of which feature a large number of processing elements laid out…
HAO: Hardware-aware neural Architecture Optimization for Efficient Inference
Zhen Dong, Yizhao Gao, Qijing Huang +3
Automatic algorithm-hardware co-design for DNN has shown great success in improving the performance of DNNs on FPGAs. However, this process remains challenging due to the intractab…
CoDeNet: Efficient Deployment of Input-Adaptive Object Detection on Embedded FPGAs
Zhen Dong, Dequan Wang, Qijing Huang +6
Deploying deep learning models on embedded systems has been challenging due to limited computing resources. The majority of existing work focuses on accelerating image classificati…
ProTuner: Tuning Programs with Monte Carlo Tree Search
Ameer Haj-Ali, Hasan Genc, Qijing Huang +4
We explore applying the Monte Carlo Tree Search (MCTS) algorithm in a notoriously difficult task: tuning programs for high-performance deep learning and image processing. We build…
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
Qijing Huang, Ameer Haj-Ali, William Moses +4
The performance of the code a compiler generates depends on the order in which it applies the optimization passes. Choosing a good order--often referred to as the phase-ordering pr…
Algorithm-hardware Co-design for Deformable Convolution
Qijing Huang, Dequan Wang, Yizhao Gao +5
FPGAs provide a flexible and efficient platform to accelerate rapidly-changing algorithms for computer vision. The majority of existing work focuses on accelerating image classific…