4 citations · 9 across the 10 of their papers we have counts for
11 papers
Interface-Aware KV Cache Quantization for Dense On-Chip NVM in Long-Context LLM Decoding
Jiahao Zheng, Yifan Qin, Xiaobo Sharon Hu +1
The key-value (KV) cache is the dominant memory bottleneck in long-context large language model (LLM) decoding: every step reads it entirely, so decoding is memory-bandwidth bound.…
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…
TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Accelerators
Yifan Qin, Zheyu Yan, Zixuan Pan +3
Compute-in-memory (CIM) accelerators using non-volatile memory (NVM) devices offer promising solutions for energy-efficient and low-latency Deep Neural Network (DNN) inference exec…
U-SWIM: Universal Selective Write-Verify for Computing-in-Memory Neural Accelerators
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Architectures that incorporate Computing-in-Memory (CiM) using emerging non-volatile memory (NVM) devices have become strong contenders for deep neural network (DNN) acceleration d…
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…
Improving Realistic Worst-Case Performance of NVCiM DNN Accelerators through Training with Right-Censored Gaussian Noise
Zheyu Yan, Yifan Qin, Wujie Wen +2
Compute-in-Memory (CiM), built upon non-volatile memory (NVM) devices, is promising for accelerating deep neural networks (DNNs) owing to its in-situ data processing capability and…
On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators
Zheyu Yan, Yifan Qin, Xiaobo Sharon Hu +1
Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to strin…
NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators
Yifan Qin, Zheyu Yan, Dailin Gan +5
Compute-in-memory accelerators built upon non-volatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in…
ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference
Jing Gong, Hassaan Saadat, Hasindu Gamaarachchi +3
Edge training of Deep Neural Networks (DNNs) is a desirable goal for continuous learning; however, it is hindered by the enormous computational power required by training. Hardware…
Agent-Centric Relation Graph for Object Visual Navigation
Xiaobo Hu, Youfang Lin, Shuo Wang +2
Object visual navigation aims to steer an agent toward a target object based on visual observations. It is highly desirable to reasonably perceive the environment and accurately co…
Towards Privacy-aware Task Allocation in Social Sensing based Edge Computing Systems
Daniel Zhang, Yue Ma, X. Sharon Hu +1
With the advance in mobile computing, Internet of Things, and ubiquitous wireless connectivity, social sensing based edge computing (SSEC) has emerged as a new computation paradigm…