6 citations · 11 across the 5 of their papers we have counts for
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cs.LG2024★ 4 cited
Squat: Quant Small Language Models on the Edge
Xuan Shen, Peiyan Dong, Zhenglun Kong +9
A growing trend has emerged in designing high-quality Small Language Models (SLMs) with a few million parameters. This trend is driven by the increasing concerns over cloud costs,…
cs.LG2024
EdgeOL: Efficient in-situ Online Learning on Edge Devices
Sheng Li, Geng Yuan, Yue Dai +10
Emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and naturally require: i) handling streaming-i…
cs.LG2023★ 1 cited
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
Xuan Shen, Peiyan Dong, Lei Lu +5
Large Language Models (LLMs) stand out for their impressive performance in intricate language modeling tasks. However, their demanding computational and memory needs pose obstacles…