1 citations · 1 across the 14 of their papers we have counts for
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SkipCat: Rank-Maximized Low-Rank Compression of Large Language Models via Shared Projection and Block Skipping
Yu-Chen Lu, Sheng-Feng Yu, Hui-Hsien Weng +5
Large language models (LLM) have achieved remarkable performance across a wide range of tasks. However, their substantial parameter sizes pose significant challenges for deployment…
UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
Hung-Yueh Chiang, Chi-Chih Chang, Yu-Chen Lu +4
Deploying large language models (LLMs) on mobile platforms faces significant challenges due to the limited memory and shared computational resources of the device. Resource availab…
FLRC: Fine-grained Low-Rank Compressor for Efficient LLM Inference
Yu-Chen Lu, Chong-Yan Chen, Chi-Chih Chang +2
Although large language models (LLM) have achieved remarkable performance, their enormous parameter counts hinder deployment on resource-constrained hardware. Low-rank compression…
Speculate Deep and Accurate: Lossless and Training-Free Acceleration for Offloaded LLMs via Substitute Speculative Decoding
Pei-Shuo Wang, Jian-Jia Chen, Chun-Che Yang +4
The immense model sizes of large language models (LLMs) challenge deployment on memory-limited consumer GPUs. Although model compression and parameter offloading are common strateg…
QuarterMap: Efficient Post-Training Token Pruning for Visual State Space Models
Tien-Yu Chi, Hung-Yueh Chiang, Diana Marculescu +1
State space models (SSMs) reduce the quadratic complexity of transformers by leveraging linear recurrence. Recently, VMamba has emerged as a strong SSM-based vision backbone, yet r…
Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin +3
State Space Models (SSMs) are emerging as a compelling alternative to Transformers because of their consistent memory usage and high performance. Despite this, scaling up SSMs on c…
Systolic Sparse Tensor Slices: FPGA Building Blocks for Sparse and Dense AI Acceleration
Endri Taka, Ning-Chi Huang, Chi-Chih Chang +3
FPGA architectures have recently been enhanced to meet the substantial computational demands of modern deep neural networks (DNNs). To this end, both FPGA vendors and academic rese…