20 citations · 54 across the 13 of their papers we have counts for
13 papers
AIM: Software and Hardware Co-design for Architecture-level IR-drop Mitigation in High-performance PIM
Yuanpeng Zhang, Xing Hu, Xi Chen +10
SRAM Processing-in-Memory (PIM) has emerged as the most promising implementation for high-performance PIM, delivering superior computing density, energy efficiency, and computation…
Algorithm-hardware co-design for Energy-Efficient A/D conversion in ReRAM-based accelerators
Chenguang Zhang, Zhihang Yuan, Xingchen Li +1
Deep neural networks are widely deployed in many fields. Due to the in-situ computation (known as processing in memory) capacity of the Resistive Random Access Memory (ReRAM) cross…
Latency-aware Unified Dynamic Networks for Efficient Image Recognition
Yizeng Han, Zeyu Liu, Zhihang Yuan +4
Dynamic computation has emerged as a promising avenue to enhance the inference efficiency of deep networks. It allows selective activation of computational units, leading to a redu…
RPTQ: Reorder-based Post-training Quantization for Large Language Models
Zhihang Yuan, Lin Niu, Jiawei Liu +7
Large-scale language models (LLMs) have demonstrated impressive performance, but their deployment presents challenges due to their significant memory usage. This issue can be allev…
Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric
Lin Niu, Jiawei Liu, Zhihang Yuan +3
Efficient inference for object detection networks is a major challenge on edge devices. Post-Training Quantization (PTQ), which transforms a full-precision model into low bit-width…
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Zhihang Yuan, Jiawei Liu, Jiaxiang Wu +6
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures. Despite its e…
PD-Quant: Post-Training Quantization based on Prediction Difference Metric
Jiawei Liu, Lin Niu, Zhihang Yuan +3
Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it…
Post-training Quantization on Diffusion Models
Yuzhang Shang, Zhihang Yuan, Bin Xie +2
Denoising diffusion (score-based) generative models have recently achieved significant accomplishments in generating realistic and diverse data. These approaches define a forward d…
Latency-aware Spatial-wise Dynamic Networks
Yizeng Han, Zhihang Yuan, Yifan Pu +4
Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixe…
PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization
Zhihang Yuan, Chenhao Xue, Yiqi Chen +2
Quantization is one of the most effective methods to compress neural networks, which has achieved great success on convolutional neural networks (CNNs). Recently, vision transforme…
PTQ-SL: Exploring the Sub-layerwise Post-training Quantization
Zhihang Yuan, Yiqi Chen, Chenhao Xue +3
Network quantization is a powerful technique to compress convolutional neural networks. The quantization granularity determines how to share the scaling factors in weights, which a…
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference
Zhihang Yuan, Xin Liu, Bingzhe Wu +1
Dynamic inference is a feasible way to reduce the computational cost of convolutional neural network(CNN), which can dynamically adjust the computation for each input sample. One o…
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
Zhihang Yuan, Bingzhe Wu, Zheng Liang +3
Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN). In contrast to static methods (e.g. weight p…