1 citations · 1 across the 3 of their papers we have counts for
4 papers
Precision As You Need: Stochastic Computing Is a Dense Adaptive Quantizer
Haoran Jin, Kangqi Zhang, Jirong Yang +4
Matrix multiplications dominate the inference cost of modern transformer-based vision models, yet existing efficiency techniques such as post-training quantization and mixed-precis…
Rethinking Agentic Kernel Generation for Emerging Accelerators
Ruijie Gao, Jirong Yang, Barry Lyu +2
Emerging accelerators often lack mature compiler backends, motivating neural agents that generate and repair kernels from architectural documentation and simulator feedback. This a…
TensorLift: Automatic Extraction of Tensor-Level ISA Semantics from Accelerator RTL via MLIR Semantic Lifting
Ruijie Gao, Haoran Jin, Jirong Yang +1
Most proposed tensor accelerators lack well-documented ISAs and compiler backends, and are exercised only through hand-written kernels covering a handful of operators. Recent work…
EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting
Zhongzhi Yu, Zheng Wang, Yuhan Li +6
Efficient adaption of large language models (LLMs) on edge devices is essential for applications requiring continuous and privacy-preserving adaptation and inference. However, exis…