20 citations · 40 across the 9 of their papers we have counts for
9 papers
MRMAD: A Multi-Round Multi-Audio Benchmark for Evaluating Acoustic Degradation Perception in Large Audio-Language Models
Yize Li, Ningyuan Yang, Sile Yin +6
Large audio-language models (LALMs) have shown promising progress in understanding speech, music, and general sound events, yet their ability to reason about how audio signals are…
SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum-Flux-Parametron Superconducting Circuits
Yanyue Xie, Peiyan Dong, Geng Yuan +10
Superconducting circuits, like Adiabatic Quantum-Flux-Parametron (AQFP), offer exceptional energy efficiency but face challenges in physical design due to sophisticated spacing and…
Language model compression with weighted low-rank factorization
Yen-Chang Hsu, Ting Hua, Sungen Chang +3
Factorizing a large matrix into small matrices is a popular strategy for model compression. Singular value decomposition (SVD) plays a vital role in this compression strategy, appr…
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
Sung-En Chang, Yanyu Li, Mengshu Sun +2
This work targets the commonly used FPGA (field-programmable gate array) devices as the hardware platform for DNN edge computing. We focus on DNN quantization as the main model com…
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
Sung-En Chang, Yanyu Li, Mengshu Sun +4
This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the firs…
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Shaoyi Huang, Dongkuan Xu, Ian E. H. Yen +8
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However,…
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework
Sung-En Chang, Yanyu Li, Mengshu Sun +5
Deep Neural Networks (DNNs) have achieved extraordinary performance in various application domains. To support diverse DNN models, efficient implementations of DNN inference on edg…
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework
Sung-En Chang, Yanyu Li, Mengshu Sun +4
With the tremendous success of deep learning, there exists imminent need to deploy deep learning models onto edge devices. To tackle the limited computing and storage resources in…
Efficient Tensor Decomposition with Boolean Factors
Sung-En Chang, Xun Zheng, Ian E. H. Yen +2
Tensor decomposition has been extensively used as a tool for exploratory analysis. Motivated by neuroscience applications, we study tensor decomposition with Boolean factors. The r…