1 citations · 1 across the 4 of their papers we have counts for
4 papers
Bandwidth-Efficient Adaptive Mixture-of-Experts via Low-Rank Compensation
Zhenyu Liu, Yunzhen Liu, Zehao Fan +5
Mixture-of-Experts (MoE) models scale capacity via sparse activation but stress memory and bandwidth. Offloading alleviates GPU memory by fetching experts on demand, yet token-leve…
Context-Aware Mixture-of-Experts Inference on CXL-Enabled GPU-NDP Systems
Zehao Fan, Zhenyu Liu, Yunzhen Liu +4
Mixture-of-Experts (MoE) models scale large language models through conditional computation, but inference becomes memory-bound once expert weights exceed the capacity of GPU memor…
Sparse Attention Remapping with Clustering for Efficient LLM Decoding on PIM
Zehao Fan, Garrett Gagnon, Zhenyu Liu +1
Transformer-based models are the foundation of modern machine learning, but their execution, particularly during autoregressive decoding in large language models (LLMs), places sig…
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons
Zhenyu Liu, Garrett Gagnon, Swagath Venkataramani +1
Deep Neural Networks (DNNs) have revolutionized a wide range of industries, from healthcare and finance to automotive, by offering unparalleled capabilities in data analysis and de…