11 papers
PADE: A Predictor-Free Sparse Attention Accelerator via Unified Execution and Stage Fusion
Huizheng Wang, Hongbin Wang, Zichuan Wang +5
Attention-based models have revolutionized AI, but the quadratic cost of self-attention incurs severe computational and memory overhead. Sparse attention methods alleviate this by…
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…
cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution
Jinwu Chen, Qidie Wu, Bin Li +5
Optimizing CUDA kernels is a challenging and labor-intensive task, given the need for hardware-software co-design expertise and the proprietary nature of high-performance kernel li…
TEMP: A Memory Efficient Physical-aware Tensor Partition-Mapping Framework on Wafer-scale Chips
Huizheng Wang, Taiquan Wei, Zichuan Wang +8
Large language models (LLMs) demand significant memory and computation resources. Wafer-scale chips (WSCs) provide high computation power and die-to-die (D2D) bandwidth but face a…
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…