58 citations · 75 across the 7 of their papers we have counts for
4 papers · 1 filter
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…