22 citations · 113 across the 46 of their papers we have counts for
13 papers · 1 filter
DiffLUT-Net: Differentiable Training of FPGA LUT Networks with Learnable Connectivity
Jiaqi Ye, Xinrui Gong, Jingcun Wang +3
Field-programmable gate arrays (FPGAs) enable efficient neural-network inference, but most deployment flows either accelerate multiply-accumulate operations or convert pretrained q…
Different Prompts, Different Ranks: Prompt-aware Dynamic Rank Selection for SVD-based LLM Compression
Hengyi Zhu, Zhendong Mi, Grace Li Zhang +1
Large language models (LLMs) have rapidly grown in scale, creating substantial memory and computational costs that hinder efficient deployment. Singular value decomposition (SVD) h…
KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs
Chuangtao Chen, Grace Li Zhang, Xunzhao Yin +3
Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: reusing a cached document in a…
OptINC: Optical In-Network-Computing for Scalable Distributed Learning
Sijie Fei, Grace Li Zhang, Bing Li +1
Distributed learning is widely used for training large models on large datasets by distributing parts of the model or dataset across multiple devices and aggregating the computed r…
Late Breaking Results: Conversion of Neural Networks into Logic Flows for Edge Computing
Daniel Stein, Shaoyi Huang, Rolf Drechsler +2
Neural networks have been successfully applied in various resource-constrained edge devices, where usually central processing units (CPUs) instead of graphics processing units exis…
Towards Fast LLM Fine-tuning through Zeroth-Order Optimization with Projected Gradient-Aligned Perturbations
Zhendong Mi, Qitao Tan, Grace Li Zhang +3
Fine-tuning large language models (LLMs) using zeroth-order (ZO) optimization has emerged as a promising alternative to traditional gradient-based methods due to its reduced memory…
Layer-wise dynamic rank for compressing large language models
Zhendong Mi, Bian Sun, Grace Li Zhang +1
Large language models (LLMs) have rapidly scaled in size, bringing severe memory and computational challenges that hinder their deployment. Singular Value Decomposition (SVD)-based…
LLM-NAS: LLM-driven Hardware-Aware Neural Architecture Search
Hengyi Zhu, Grace Li Zhang, Shaoyi Huang
Hardware-Aware Neural Architecture Search (HW-NAS) requires joint optimization of accuracy and latency under device constraints. Traditional supernet-based methods require multiple…
Revolution or Hype? Seeking the Limits of Large Models in Hardware Design
Qiang Xu, Leon Stok, Rolf Drechsler +3
Recent breakthroughs in Large Language Models (LLMs) and Large Circuit Models (LCMs) have sparked excitement across the electronic design automation (EDA) community, promising a re…
Early-Exit with Class Exclusion for Efficient Inference of Neural Networks
Jingcun Wang, Bing Li, Grace Li Zhang
Deep neural networks (DNNs) have been successfully applied in various fields. In DNNs, a large number of multiply-accumulate (MAC) operations are required to be performed, posing c…
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks
Chuangtao Chen, Grace Li Zhang, Xunzhao Yin +3
Deep neural networks (DNNs) have been widely deployed across diverse domains such as computer vision and natural language processing. However, the impressive accomplishments of DNN…
Class-based Quantization for Neural Networks
Wenhao Sun, Grace Li Zhang, Huaxi Gu +2
In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC) operations. Accordingly, it is challenging to apply DNNs on resource-constraine…
SteppingNet: A Stepping Neural Network with Incremental Accuracy Enhancement
Wenhao Sun, Grace Li Zhang, Xunzhao Yin +4
Deep neural networks (DNNs) have successfully been applied in many fields in the past decades. However, the increasing number of multiply-and-accumulate (MAC) operations in DNNs pr…