no code implementations • 14 Oct 2023 • Zhepeng Wang, Isaacshubhanand Putla, Weiwen Jiang, Youzuo Lin
Seismic full waveform inversion (FWI) is a widely used technique in geophysics for inferring subsurface structures from seismic data.
no code implementations • 26 Aug 2023 • Yi Sheng, Junhuan Yang, Lei Yang, Yiyu Shi, Jingtongf Hu, Weiwen Jiang
Model fairness (a. k. a., bias) has become one of the most critical problems in a wide range of AI applications.
no code implementations • 19 Jul 2023 • Jinyang Li, Zhepeng Wang, Zhirui Hu, Prasanna Date, Ang Li, Weiwen Jiang
The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers.
no code implementations • 23 Apr 2023 • Zhepeng Wang, Jinyang Li, Zhirui Hu, Blake Gage, Elizabeth Iwasawa, Weiwen Jiang
We further developed a reinforcement learning-based security engine, which can automatically optimize the model design under the distributed setting, such that a good trade-off between model performance and security can be made.
no code implementations • 24 Feb 2023 • Junhuan Yang, Yi Sheng, Yuzhou Zhang, Weiwen Jiang, Lei Yang
What's more, for a larger size image in the BBBC005 dataset, the existing approach cannot be accommodated to Raspberry PI due to out of memory; on the other hand, SegHDC can obtain segmentation results within 3 minutes while achieving a 0. 9587 IoU score.
no code implementations • 9 Dec 2022 • Yifan Gong, Zheng Zhan, Pu Zhao, Yushu Wu, Chao Wu, Caiwen Ding, Weiwen Jiang, Minghai Qin, Yanzhi Wang
By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i. e., keeping the difference in speed performance under various execution frequencies as small as possible.
1 code implementation • 30 Oct 2022 • Hanrui Wang, Pengyu Liu, Jinglei Cheng, Zhiding Liang, Jiaqi Gu, Zirui Li, Yongshan Ding, Weiwen Jiang, Yiyu Shi, Xuehai Qian, David Z. Pan, Frederic T. Chong, Song Han
Specifically, the TorchQuantum library also supports using data-driven ML models to solve problems in quantum system research, such as predicting the impact of quantum noise on circuit fidelity and improving the quantum circuit compilation efficiency.
1 code implementation • 8 Oct 2022 • Deniz Gurevin, Mohsin Shan, Tong Geng, Weiwen Jiang, Caiwen Ding, Omer Khan
Prior work operates on pre-collected temporal graph data and is not designed to handle updates on a graph in real-time.
1 code implementation • 11 Sep 2022 • Hongwu Peng, Deniz Gurevin, Shaoyi Huang, Tong Geng, Weiwen Jiang, Omer Khan, Caiwen Ding
In this paper, we utilize two state-of-the-art model compression methods (1) train and prune and (2) sparse training for the sparsification of weight layers in GNNs.
no code implementations • 7 Aug 2022 • Hongwu Peng, Shaoyi Huang, Shiyang Chen, Bingbing Li, Tong Geng, Ang Li, Weiwen Jiang, Wujie Wen, Jinbo Bi, Hang Liu, Caiwen Ding
Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm.
no code implementations • 4 Jul 2022 • Zhirui Hu, Peiyan Dong, Zhepeng Wang, Youzuo Lin, Yanzhi Wang, Weiwen Jiang
Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices.
no code implementations • 5 May 2022 • Boyang Li, Qing Lu, Weiwen Jiang, Taeho Jung, Yiyu Shi
In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload, thus the computation power of miners will not purely be spent on the hash puzzle.
no code implementations • 23 Feb 2022 • Yi Sheng, Junhuan Yang, Yawen Wu, Kevin Mao, Yiyu Shi, Jingtong Hu, Weiwen Jiang, Lei Yang
Results show that FaHaNa can identify a series of neural networks with higher fairness and accuracy on a dermatology dataset.
no code implementations • 11 Feb 2022 • Junhuan Yang, Yi Sheng, Sizhe Zhang, Ruixuan Wang, Kenneth Foreman, Mikell Paige, Xun Jiao, Weiwen Jiang, Lei Yang
On the Clintox dataset, which tries to learn features from developed drugs that passed/failed clinical trials for toxicity reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC scores, which are 0. 80% higher than the manually designed HDC and 9. 75% higher than conventional neural networks.
1 code implementation • 1 Nov 2021 • Bingqian Lu, Jianyi Yang, Weiwen Jiang, Yiyu Shi, Shaolei Ren
A key requirement of efficient hardware-aware NAS is the fast evaluation of inference latencies in order to rank different architectures.
Hardware Aware Neural Architecture Search Neural Architecture Search
no code implementations • ICCV 2021 • Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Sijia Liu, Yanzhi Wang, Xue Lin
Specifically, this is the first effort to assign mixed quantization schemes and multiple precisions within layers -- among rows of the DNN weight matrix, for simplified operations in hardware inference, while preserving accuracy.
no code implementations • 15 Oct 2021 • Bingbing Li, Hongwu Peng, Rajat Sainju, Junhuan Yang, Lei Yang, Yueying Liang, Weiwen Jiang, Binghui Wang, Hang Liu, Caiwen Ding
In this paper, we propose a novel gender bias detection method by utilizing attention map for transformer-based models.
no code implementations • 13 Sep 2021 • Zheyu Yan, Weiwen Jiang, Xiaobo Sharon Hu, Yiyu Shi
To the best of the authors' knowledge, this is the first DNAS framework that can handle large search spaces with bounded memory usage.
no code implementations • 8 Sep 2021 • Zhiding Liang, Zhepeng Wang, Junhuan Yang, Lei Yang, JinJun Xiong, Yiyu Shi, Weiwen Jiang
Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise.
no code implementations • 8 Sep 2021 • Zhepeng Wang, Zhiding Liang, Shanglin Zhou, Caiwen Ding, Yiyu Shi, Weiwen Jiang
Experimental results demonstrate that the identified quantum neural architectures with mixed quantum neurons can achieve 90. 62% of accuracy on the MNIST dataset, compared with 52. 77% and 69. 92% on the VQC and QuantumFlow, respectively.
no code implementations • 30 May 2021 • Wei Niu, Zhenglun Kong, Geng Yuan, Weiwen Jiang, Jiexiong Guan, Caiwen Ding, Pu Zhao, Sijia Liu, Bin Ren, Yanzhi Wang
In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model to meet both resource and real-time specifications of mobile devices.
no code implementations • 12 Feb 2021 • Yuhong Song, Weiwen Jiang, Bingbing Li, Panjie Qi, Qingfeng Zhuge, Edwin Hsing-Mean Sha, Sakyasingha Dasgupta, Yiyu Shi, Caiwen Ding
Specifically, RT3 integrates two-level optimizations: First, it utilizes an efficient BP as the first-step compression for resource-constrained mobile devices; then, RT3 heuristically generates a shrunken search space based on the first level optimization and searches multiple pattern sets with diverse sparsity for PP via reinforcement learning to support lightweight software reconfiguration, which corresponds to available frequency levels of DVFS (i. e., hardware reconfiguration).
no code implementations • 1 Jan 2021 • Weiwen Jiang, Yukun Ding, Yiyu Shi
With the continuously increasing number of quantum bits in quantum computers, there are growing interests in exploring applications that can harvest the power of them.
no code implementations • 1 Jan 2021 • Qing Lu, Weiwen Jiang, Meng Jiang, Jingtong Hu, Sakyasingha Dasgupta, Yiyu Shi
The success of gragh neural networks (GNNs) in the past years has aroused grow-ing interest and effort in designing best models to handle graph-structured data.
3 code implementations • 18 Dec 2020 • Weiwen Jiang, JinJun Xiong, Yiyu Shi
It is imminent to know how to design the quantum circuit for accelerating neural networks.
no code implementations • 28 Oct 2020 • Yongan Zhang, Yonggan Fu, Weiwen Jiang, Chaojian Li, Haoran You, Meng Li, Vikas Chandra, Yingyan Lin
Powerful yet complex deep neural networks (DNNs) have fueled a booming demand for efficient DNN solutions to bring DNN-powered intelligence into numerous applications.
no code implementations • 16 Sep 2020 • Sung-En Chang, Yanyu Li, Mengshu Sun, Weiwen Jiang, Runbin Shi, Xue Lin, Yanzhi Wang
To tackle the limited computing and storage resources in edge devices, model compression techniques have been widely used to trim deep neural network (DNN) models for on-device inference execution.
no code implementations • 15 Sep 2020 • Wei Niu, Zhenglun Kong, Geng Yuan, Weiwen Jiang, Jiexiong Guan, Caiwen Ding, Pu Zhao, Sijia Liu, Bin Ren, Yanzhi Wang
Our framework can guarantee the identified model to meet both resource and real-time specifications of mobile devices, thus achieving real-time execution of large transformer-based models like BERT variants.
no code implementations • 17 Aug 2020 • Dewen Zeng, Weiwen Jiang, Tianchen Wang, Xiaowei Xu, Haiyun Yuan, Meiping Huang, Jian Zhuang, Jingtong Hu, Yiyu Shi
Experimental results on ACDC MICCAI 2017 dataset demonstrate that our hardware-aware multi-scale NAS framework can reduce the latency by up to 3. 5 times and satisfy the real-time constraints, while still achieving competitive segmentation accuracy, compared with the state-of-the-art NAS segmentation framework.
1 code implementation • 17 Jul 2020 • Weiwen Jiang, Lei Yang, Sakyasingha Dasgupta, Jingtong Hu, Yiyu Shi
To tackle this issue, HotNAS builds a chain of tools to design hardware to support compression, based on which a global optimizer is developed to automatically co-search all the involved search spaces.
no code implementations • 14 Jul 2020 • Song Bian, Xiaowei Xu, Weiwen Jiang, Yiyu Shi, Takashi Sato
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data.
no code implementations • 13 Jul 2020 • Xingang Yan, Weiwen Jiang, Yiyu Shi, Cheng Zhuo
The recent breakthroughs of Neural Architecture Search (NAS) have motivated various applications in medical image segmentation.
3 code implementations • 26 Jun 2020 • Weiwen Jiang, JinJun Xiong, Yiyu Shi
We discover that, in order to make full use of the strength of quantum representation, it is best to represent data in a neural network as either random variables or numbers in unitary matrices, such that they can be directly operated by the basic quantum logical gates.
no code implementations • 10 Feb 2020 • Lei Yang, Zheyu Yan, Meng Li, Hyoukjun Kwon, Liangzhen Lai, Tushar Krishna, Vikas Chandra, Weiwen Jiang, Yiyu Shi
Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs).
no code implementations • 30 Jan 2020 • Song Bian, Weiwen Jiang, Qing Lu, Yiyu Shi, Takashi Sato
Due to increasing privacy concerns, neural network (NN) based secure inference (SI) schemes that simultaneously hide the client inputs and server models attract major research interests.
no code implementations • 31 Oct 2019 • Qing Lu, Weiwen Jiang, Xiaowei Xu, Yiyu Shi, Jingtong Hu
With 30, 000 LUTs, a light-weight design is found to achieve 82. 98\% accuracy and 1293 images/second throughput, compared to which, under the same constraints, the traditional method even fails to find a valid solution.
no code implementations • 31 Oct 2019 • Weiwen Jiang, Qiuwen Lou, Zheyu Yan, Lei Yang, Jingtong Hu, Xiaobo Sharon Hu, Yiyu Shi
In this paper, we are the first to bring the computing-in-memory architecture, which can easily transcend the memory wall, to interplay with the neural architecture search, aiming to find the most efficient neural architectures with high network accuracy and maximized hardware efficiency.
1 code implementation • 6 Jul 2019 • Weiwen Jiang, Lei Yang, Edwin Sha, Qingfeng Zhuge, Shouzhen Gu, Sakyasingha Dasgupta, Yiyu Shi, Jingtong Hu
We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS).
no code implementations • 31 Jan 2019 • Weiwen Jiang, Xinyi Zhang, Edwin H. -M. Sha, Lei Yang, Qingfeng Zhuge, Yiyu Shi, Jingtong Hu
In addition, with a performance abstraction model to analyze the latency of neural architectures without training, our framework can quickly prune architectures that do not satisfy the specification, leading to higher efficiency.