40 citations · 43 across the 10 of their papers we have counts for
11 papers
LocalTrain: Integer-Only Local Learning for Energy-Efficient CNN Training on Edge FPGAs
Shiqi Ding, Xuanyu Liang, Xinyang Liu +4
Local learning reduces cross-layer training dependencies, but an efficient accelerator must still retain operands, accumulate gradients, and select quantization scales. We present…
MAPLE: MoE Adaptive Plug-and-play Layer-wise Expert allocation
Lie Li, Wen Li, Junxiao Shen +1
Sparsely-activated Mixture-of-Experts (MoE) Transformers universally fix the same number of routed experts across all layers, a convention that ignores the well-documented heteroge…
SPEAR: A System for Post-Quantization Error-Adaptive Recovery Enabling Efficient Low-Bit LLM Serving
Hongyuan Liu, Yawei Li, Zhiqiang Que +3
Efficient large language model (LLM) serving is increasingly constrained by deployment cost. Quantization is a key technique for reducing serving cost, yet even state-of-the-art 4-…
FFR: Forward-Forward Learning for Regression
Xinyang Liu, Xuanyu Liang, Shiqi Ding +4
The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely loca…
ColorConceptBench: A Benchmark for Probabilistic Color-Concept Understanding in Text-to-Image Models
Chenxi Ruan, Yihan Hou, Yu Xiao +2
Text-to-image (T2I) models have advanced considerably in generating high-quality images from textual descriptions. However, their ability to associate colors with concepts remains…
Metrics and evaluations for computational and sustainable AI efficiency
Hongyuan Liu, Xinyang Liu, Guosheng Hu
The rapid advancement of Artificial Intelligence (AI) has created unprecedented demands for computational power, yet methods for evaluating the performance, efficiency, and environ…
Towards Extreme Pruning of LLMs with Plug-and-Play Mixed Sparsity
Chi Xu, Gefei Zhang, Yantong Zhu +4
N:M structured pruning is essential for large language models (LLMs) because it can remove less important network weights and reduce the memory and computation requirements. Existi…
Revisiting Large Language Model Pruning using Neuron Semantic Attribution
Yizhuo Ding, Xinwei Sun, Yanwei Fu +1
Model pruning technique is vital for accelerating large language models by reducing their size and computational requirements. However, the generalizability of existing pruning met…
Gradient-Guided Modality Decoupling for Missing-Modality Robustness
Hao Wang, Shengda Luo, Guosheng Hu +1
Multimodal learning with incomplete input data (missing modality) is practical and challenging. In this work, we conduct an in-depth analysis of this challenge and find that modali…
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training
Yuyuan Feng, Guosheng Hu, Xiaodong Li +1
Health modeling of lithium-ion batteries (LIBs) is crucial for safe and efficient energy management and carries significant socio-economic implications. Although Machine Learning (…
DADA: Differentiable Automatic Data Augmentation
Yonggang Li, Guosheng Hu, Yongtao Wang +3
Data augmentation (DA) techniques aim to increase data variability, and thus train deep networks with better generalisation. The pioneering AutoAugment automated the search for opt…