58 citations · 75 across the 7 of their papers we have counts for
7 papers
Low Power Vision Transformer Accelerator with Hardware-Aware Pruning and Optimized Dataflow
Ching-Lin Hsiung, Tian-Sheuan Chang
Current transformer accelerators primarily focus on optimizing self-attention due to its quadratic complexity. However, this focus is less relevant for vision transformers with sho…
A 71.2-W Speech Recognition Accelerator with Recurrent Spiking Neural Network
Chih-Chyau Yang, Tian-Sheuan Chang
This paper introduces a 71.2-W speech recognition accelerator designed for edge devices' real-time applications, emphasizing an ultra low power design. Achieved through algorith…
A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices
Ci-Hao Wu, Tian-Sheuan Chang
Transformer-based speech enhancement models yield impressive results. However, their heterogeneous and complex structure restricts model compression potential, resulting in greater…
ESSR: An 8K@30FPS Super-Resolution Accelerator With Edge Selective Network
Chih-Chia Hsu, Tian-Sheuan Chang
Deep learning-based super-resolution (SR) is challenging to implement in resource-constrained edge devices for resolutions beyond full HD due to its high computational complexity a…
IQNet: Image Quality Assessment Guided Just Noticeable Difference Prefiltering For Versatile Video Coding
Yu-Han Sun, Chiang Lo-Hsuan Lee, Tian-Sheuan Chang
Image prefiltering with just noticeable distortion (JND) improves coding efficiency in a visual lossless way by filtering the perceptually redundant information prior to compressio…
ASC: Adaptive Scale Feature Map Compression for Deep Neural Network
Yuan Yao, Tian-Sheuan Chang
Deep-learning accelerators are increasingly in demand; however, their performance is constrained by the size of the feature map, leading to high bandwidth requirements and large bu…
RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Clouds
Tzu-Hsuan Chen, Tian Sheuan Chang
Semantic outdoor scene understanding based on 3D LiDAR point clouds is a challenging task for autonomous driving due to the sparse and irregular data structure. This paper takes ad…