3 citations · 5 across the 14 of their papers we have counts for
6 papers · 1 filter
When Small Variations Become Big Failures: Reliability Challenges in Compute-in-Memory Neural Accelerators
Yifan Qin, Jiahao Zheng, Zheyu Yan +3
Compute-in-memory (CiM) architectures promise significant improvements in energy efficiency and throughput for deep neural network acceleration by alleviating the von Neumann bottl…
NVCiM-PT: An NVCiM-assisted Prompt Tuning Framework for Edge LLMs
Ruiyang Qin, Pengyu Ren, Zheyu Yan +7
Large Language Models (LLMs) deployed on edge devices, known as edge LLMs, need to continuously fine-tune their model parameters from user-generated data under limited resource con…
Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices
Ruiyang Qin, Dancheng Liu, Chenhui Xu +9
The scaling laws have become the de facto guidelines for designing large language models (LLMs), but they were studied under the assumption of unlimited computing resources for bot…
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures
Ruiyang Qin, Zheyu Yan, Dewen Zeng +8
Large Language Models (LLMs) deployed on edge devices learn through fine-tuning and updating a certain portion of their parameters. Although such learning methods can be optimized…
FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models
Ruiyang Qin, Yuting Hu, Zheyu Yan +3
Neural Architecture Search (NAS) has become the de fecto tools in the industry in automating the design of deep neural networks for various applications, especially those driven by…
Compute-in-Memory based Neural Network Accelerators for Safety-Critical Systems: Worst-Case Scenarios and Protections
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Emerging non-volatile memory (NVM)-based Computing-in-Memory (CiM) architectures show substantial promise in accelerating deep neural networks (DNNs) due to their exceptional energ…