9 papers
VEGAS: Mitigating Hallucinations in Large Vision-Language Models via Vision-Encoder Attention Guided Adaptive Steering
Zihu Wang, Boxun Xu, Yuxuan Xia +1
Large vision-language models (LVLMs) exhibit impressive ability to jointly reason over visual and textual inputs. However, they often produce outputs that are linguistically fluent…
AMS-KV: Adaptive KV Caching in Multi-Scale Visual Autoregressive Transformers
Boxun Xu, Yu Wang, Zihu Wang +1
Visual autoregressive modeling (VAR) via next-scale prediction has emerged as a scalable image generation paradigm. While Key and Value (KV) caching in large language models (LLMs)…
SpikeX: Exploring Accelerator Architecture and Network-Hardware Co-Optimization for Sparse Spiking Neural Networks
Boxun Xu, Richard Boone, Peng Li
Spiking Neural Networks (SNNs) are promising biologically plausible models of computation which utilize a spiking binary activation function similar to that of biological neurons.…
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…
Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives
Zihu Wang, Boxun Xu, Hejia Geng +1
Graph contrastive learning (GCL) has demonstrated great promise for learning generalizable graph representations from unlabeled data. However, conventional GCL approaches face two…
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…