46 citations · 56 across the 13 of their papers we have counts for
4 papers · 1 filter
Bishop: Sparsified Bundling Spiking Transformers on Heterogeneous Cores with Error-Constrained Pruning
Boxun Xu, Yuxuan Yin, Vikram Iyer +1
We present Bishop, the first dedicated hardware accelerator architecture and HW/SW co-design framework for spiking transformers that optimally represents, manages, and processes sp…
Towards 3D Acceleration for low-power Mixture-of-Experts and Multi-Head Attention Spiking Transformers
Boxun Xu, Junyoung Hwang, Pruek Vanna-iampikul +3
Spiking Neural Networks(SNNs) provide a brain-inspired and event-driven mechanism that is believed to be critical to unlock energy-efficient deep learning. The mixture-of-experts a…
DS2TA: Denoising Spiking Transformer with Attenuated Spatiotemporal Attention
Boxun Xu, Hejia Geng, Yuxuan Yin +1
Vision Transformers (ViT) are current high-performance models of choice for various vision applications. Recent developments have given rise to biologically inspired spiking transf…
DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention
Boxun Xu, Hejia Geng, Yuxuan Yin +1
Among the array of neural network architectures, the Vision Transformer (ViT) stands out as a prominent choice, acclaimed for its exceptional expressiveness and consistent high per…