14 papers
MOCAP: Wafer-Scale-Chip-Oriented Memory-Orchestrated Chunked Pipelining Framework for Prefill-Only LLM Inference
Zichuan Wang, Huizheng Wang, Yuheng Xiao +6
Large language models (LLMs) are increasingly used in prefill-only workloads, where end-to-end latency is dominated by the prefill phase. For long-context prefill, communication ov…
FlexLAM: Resolving the Bottleneck Trade-off in Latent Action Learning
Takanori Yoshimoto, Yang Hu, Naruya Kondo +1
Latent actions provide a compact interface between action-free video and downstream decision-making, yet existing Latent Action Models (LAMs) force every transition through a fixed…
Beyond Similarity: Trustworthy Memory Search for Personal AI Agents
Jiawen Zhang, Kejia Chen, Jiachen Ma +7
Personal AI agents increasingly rely on long-term memory to provide persistent personalization across sessions. However, existing memory pipelines are largely driven by semantic si…
Hexcute: A Compiler Framework for Automating Layout Synthesis in GPU Programs
Xiao Zhang, Yaoyao Ding, Bolin Sun +3
Efficient GPU programming is crucial for achieving high performance in deep learning (DL) applications. The performance of GPU programs depends on how data is parallelized across t…
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…