5 citations · 14 across the 8 of their papers we have counts for
9 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.…
SGI: Structured 2D Gaussians for Efficient and Compact Large Image Representation
Zixuan Pan, Kaiyuan Tang, Jun Xia +5
2D Gaussian Splatting has emerged as a novel image representation technique that can support efficient rendering on low-end devices. However, scaling to high-resolution images requ…
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…
A 10.60 W 150 GOPS Mixed-Bit-Width Sparse CNN Accelerator for Life-Threatening Ventricular Arrhythmia Detection
Yifan Qin, Zhenge Jia, Zheyu Yan +9
This paper proposes an ultra-low power, mixed-bit-width sparse convolutional neural network (CNN) accelerator to accelerate ventricular arrhythmia (VA) detection. The chip achieves…
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…
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…
A Low-Power Accelerator for Deep Neural Networks with Enlarged Near-Zero Sparsity
Yuxiang Huan, Yifan Qin, Yantian You +2
It remains a challenge to run Deep Learning in devices with stringent power budget in the Internet-of-Things. This paper presents a low-power accelerator for processing Deep Neural…