4 papers
Designing Spatial Architectures for Sparse Attention: STAR Accelerator via Cross-Stage Tiling
Huizheng Wang, Taiquan Wei, Hongbin Wang +6
Large language models (LLMs) rely on self-attention for contextual understanding, demanding high-throughput inference and large-scale token parallelism (LTPP). Existing dynamic spa…
WATOS: Efficient LLM Training Strategies and Architecture Co-exploration for Wafer-scale Chip
Huizheng Wang, Zichuan Wang, Hongbin Wang +5
Training large language models (LLMs) imposes extreme demands on computation, memory capacity, and interconnect bandwidth, driven by their ever-increasing parameter scales and inte…
BitStopper: An Efficient Transformer Attention Accelerator via Stage-fusion and Early Termination
Huizheng Wang, Hongbin Wang, Shaojun Wei +2
Attention-based large language models (LLMs) have transformed modern AI applications, but the quadratic cost of self-attention imposes significant compute and memory overhead. Dyna…
LAPA: Log-Domain Prediction-Driven Dynamic Sparsity Accelerator for Transformer Model
Huizheng Wang, Hongbin Wang, Shaojun Wei +2
Attention-based Transformers have revolutionized natural language processing (NLP) and shown strong performance in computer vision (CV) tasks. However, as the input sequence varies…