no code implementations • 5 Mar 2024 • Zhiding Liang, Gang Liu, Zheyuan Liu, Jinglei Cheng, Tianyi Hao, Kecheng Liu, Hang Ren, Zhixin Song, Ji Liu, Fanny Ye, Yiyu Shi
In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods.
no code implementations • 20 Feb 2024 • Hao-Wei Chung, Ching-Hao Chiu, Yu-Jen Chen, Yiyu Shi, Tsung-Yi Ho
Fairness has become increasingly pivotal in machine learning for high-risk applications such as machine learning in healthcare and facial recognition.
1 code implementation • 19 Feb 2024 • Song Guo, Fan Wu, Lei Zhang, Xiawu Zheng, Shengchuan Zhang, Fei Chao, Yiyu Shi, Rongrong Ji
For instance, on the Wikitext2 dataset with LlamaV1-7B at 70% sparsity, our proposed EBFT achieves a perplexity of 16. 88, surpassing the state-of-the-art DSnoT with a perplexity of 75. 14.
no code implementations • 9 Feb 2024 • Ruiyang Qin, Yuting Hu, Zheyu Yan, JinJun Xiong, Ahmed Abbasi, Yiyu Shi
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 mobile and edge devices with limited computing resources.
no code implementations • 16 Jan 2024 • Ching-Hao Chiu, Yu-Jen Chen, Yawen Wu, Yiyu Shi, Tsung-Yi Ho
To overcome this, we propose a method enabling fair predictions for sensitive attributes during the testing phase without using such information during training.
no code implementations • 11 Dec 2023 • Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
In this study, we define the problem of pinpointing the worst-case performance of CiM DNN accelerators affected by device variations.
no code implementations • 11 Dec 2023 • Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
In our research, we illustrate that only a small fraction of weights need this write-verify treatment for the corresponding devices and the DNN accuracy can be preserved, yielding a notable programming acceleration.
no code implementations • 27 Nov 2023 • Hanrui Wang, Yilian Liu, Pengyu Liu, Jiaqi Gu, Zirui Li, Zhiding Liang, Jinglei Cheng, Yongshan Ding, Xuehai Qian, Yiyu Shi, David Z. Pan, Frederic T. Chong, Song Han
Arbitrary state preparation algorithms can be broadly categorized into arithmetic decomposition (AD) and variational quantum state preparation (VQSP).
no code implementations • 21 Nov 2023 • Ruiyang Qin, Jun Xia, Zhenge Jia, Meng Jiang, Ahmed Abbasi, Peipei Zhou, Jingtong Hu, Yiyu Shi
While it is possible to obtain annotation locally by directly asking users to provide preferred responses, such annotations have to be sparse to not affect user experience.
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.
1 code implementation • 10 Aug 2023 • Zixuan Pan, Jianxu Chen, Yiyu Shi
Denoising diffusion probabilistic models have recently demonstrated state-of-the-art generative performance and have been used as strong pixel-level representation learners.
Ranked #1 on Medical Image Segmentation on MoNuSeg
no code implementations • 30 Jul 2023 • Boyang Li, Bingyu Shen, Qing Lu, Taeho Jung, Yiyu Shi
In the conducted experiments, the PoFLSC consensus supported the subchain manager to be aware of reservation priority and the core partition of contributors to establish and maintain a competitive subchain.
no code implementations • 29 Jul 2023 • Zheyu Yan, Yifan Qin, Wujie Wen, Xiaobo Sharon Hu, Yiyu Shi
In this work, we propose to use the k-th percentile performance (KPP) to capture the realistic worst-case performance of DNN models executing on CiM accelerators.
1 code implementation • 26 Jun 2023 • Yu-Jen Chen, Xinrong Hu, Yiyu Shi, Tsung-Yi Ho
Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which is critical for patient evaluation and treatment planning.
no code implementations • 26 Jun 2023 • Ching-Hao Chiu, Hao-Wei Chung, Yu-Jen Chen, Yiyu Shi, Tsung-Yi Ho
Fairness has become increasingly pivotal in medical image recognition.
1 code implementation • 23 Jun 2023 • Xinrong Hu, Xiaowei Xu, Yiyu Shi
To evaluate the label-efficiency of our finetuning method, we compare the results of these three prediction heads on a public medical image segmentation dataset with limited labeled data.
no code implementations • 12 Jun 2023 • Zheyu Yan, Yifan Qin, Xiaobo Sharon Hu, Yiyu Shi
In this study, we present a novel approach that leverages Large Language Models (LLMs) to address this issue.
1 code implementation • 8 Jun 2023 • Yu-Jen Chen, Yiyu Shi, Tsung-Yi Ho
Magnetic resonance imaging (MRI) is a commonly used technique for brain tumor segmentation, which is critical for evaluating patients and planning treatment.
1 code implementation • 6 Jun 2023 • Xinrong Hu, Yu-Jen Chen, Tsung-Yi Ho, Yiyu Shi
Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks.
no code implementations • 31 May 2023 • Dewen Zeng, Yawen Wu, Xinrong Hu, Xiaowei Xu, Jingtong Hu, Yiyu Shi
This paper presents a new way to identify additional positive pairs for BYOL, a state-of-the-art (SOTA) self-supervised learning framework, to improve its representation learning ability.
1 code implementation • 23 May 2023 • Yifan Qin, Zheyu Yan, Wujie Wen, Xiaobo Sharon Hu, Yiyu Shi
However, the stochastic nature and intrinsic variations of NVM devices often result in performance degradation in DNN inference.
no code implementations • 9 May 2023 • Zhenge Jia, Dawei Li, Cong Liu, Liqi Liao, Xiaowei Xu, Lichuan Ping, Yiyu Shi
This paper concludes with the direction of improvement for the future TinyML design for health monitoring applications.
no code implementations • 8 Jan 2023 • Ching-Hao Chiu, Hao-Wei Chung, Yu-Jen Chen, Yiyu Shi, Tsung-Yi Ho
Fairness has become increasingly pivotal in facial recognition.
no code implementations • 16 Dec 2022 • Zhenge Jia, Yiyu Shi, Jingtong Hu, Lei Yang, Benjamin Nti
Point-of-care ultrasound (POCUS) is one of the most commonly applied tools for cardiac function imaging in the clinical routine of the emergency department and pediatric intensive care unit.
no code implementations • 2 Dec 2022 • Jiahe Shi, Yawen Wu, Dewen Zeng, Jun Tao, Jingtong Hu, Yiyu Shi
The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge.
no code implementations • 15 Nov 2022 • Yejia Zhang, Xinrong Hu, Nishchal Sapkota, Yiyu Shi, Danny Z. Chen
Self-supervised instance discrimination is an effective contrastive pretext task to learn feature representations and address limited medical image annotations.
no code implementations • 3 Nov 2022 • An Zeng, Chunbiao Wu, Meiping Huang, Jian Zhuang, Shanshan Bi, Dan Pan, Najeeb Ullah, Kaleem Nawaz Khan, Tianchen Wang, Yiyu Shi, Xiaomeng Li, Guisen Lin, Xiaowei Xu
In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images.
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.
no code implementations • 24 Aug 2022 • Yawen Wu, Dewen Zeng, Zhepeng Wang, Yi Sheng, Lei Yang, Alaina J. James, Yiyu Shi, Jingtong Hu
Self-supervised learning (SSL) methods, contrastive learning (CL) and masked autoencoders (MAE), can leverage the unlabeled data to pre-train models, followed by fine-tuning with limited labels.
no code implementations • 23 Aug 2022 • Gelei Xu, Yawen Wu, Jingtong Hu, Yiyu Shi
The framework is divided into two stages: In the first in-FL stage, clients with different skin types are trained in a federated learning process to construct a global model for all skin types.
no code implementations • 7 Aug 2022 • Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi, Jingtong Hu
However, when adopting CL in FL, the limited data diversity on each site makes federated contrastive learning (FCL) ineffective.
no code implementations • 2 Aug 2022 • Zhiding Liang, Jinglei Cheng, Hang Ren, Hanrui Wang, Fei Hua, Zhixin Song, Yongshan Ding, Fred Chong, Song Han, Xuehai Qian, Yiyu Shi
Therefore, we propose NAPA, a native-pulse ansatz generator framework for VQAs.
1 code implementation • 27 Jul 2022 • Xinrong Hu, Corey Wang, Yiyu Shi
As such, we enforce contrastive losses on the generated images and the input images to train the encoder of a segmentation model to minimize the discrepancy between paired images in the learned embedding space.
no code implementations • 15 Jul 2022 • Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
In this work, we formulate the problem of determining the worst-case performance of CiM DNN accelerators under the impact of device variations.
no code implementations • 8 Jun 2022 • Qing Lu, Xiaowei Xu, Shunjie Dong, Cong Hao, Lei Yang, Cheng Zhuo, Yiyu Shi
Accurately segmenting temporal frames of cine magnetic resonance imaging (MRI) is a crucial step in various real-time MRI guided cardiac interventions.
2 code implementations • 10 May 2022 • Yu Fu, Yanyan Huang, Yalin Wang, Shunjie Dong, Le Xue, Xunzhao Yin, Qianqian Yang, Yiyu Shi, Cheng Zhuo
In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs.
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 Apr 2022 • Yawen Wu, Dewen Zeng, Zhepeng Wang, Yiyu Shi, Jingtong Hu
However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective.
1 code implementation • 25 Mar 2022 • Bingqian Lu, Zheyu Yan, Yiyu Shi, Shaolei Ren
We first perform neural architecture search to obtain a small set of optimal architectures for one accelerator candidate.
no code implementations • 4 Mar 2022 • Yawen Wu, Dewen Zeng, Xiaowei Xu, Yiyu Shi, Jingtong Hu
By pruning the parameters based on this importance difference, we can reduce the accuracy difference between the privileged group and the unprivileged group to improve fairness without a large accuracy drop.
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.
1 code implementation • 17 Feb 2022 • Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
In this work, we show that it is only necessary to select a small portion of the weights for write-verify to maintain the DNN accuracy, thus achieving significant speedup.
no code implementations • 14 Feb 2022 • Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu
To tackle this problem, we propose a data generation framework with two methods to improve CL training by joint sample generation and contrastive learning.
no code implementations • 14 Feb 2022 • Yawen Wu, Dewen Zeng, Zhepeng Wang, Yi Sheng, Lei Yang, Alaina J. James, Yiyu Shi, Jingtong Hu
The recently developed self-supervised learning approach, contrastive learning (CL), can leverage the unlabeled data to pre-train a model, after which the model is fine-tuned on limited labeled data for dermatological disease diagnosis.
no code implementations • 21 Nov 2021 • Yawen Wu, Zhepeng Wang, Dewen Zeng, Meng Li, Yiyu Shi, Jingtong Hu
To tackle this problem, we propose a collaborative contrastive learning framework consisting of two approaches: feature fusion and neighborhood matching, by which a unified feature space among clients is learned for better data representations.
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 • 23 Oct 2021 • Yu-Jen Chen, Yen-Jung Chang, Shao-Cheng Wen, Yiyu Shi, Xiaowei Xu, Tsung-Yi Ho, Meiping Huang, Haiyun Yuan, Jian Zhuang
Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patients' characteristics, surrounding environment, etc.
no code implementations • 29 Sep 2021 • Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu
In this way, the main model learns to cluster hard positives by pulling the representations of similar yet distinct samples together, by which the representations of similar samples are well-clustered and better representations can be learned.
no code implementations • 29 Sep 2021 • Yawen Wu, Zhepeng Wang, Dewen Zeng, Meng Li, Yiyu Shi, Jingtong Hu
Federated learning (FL) enables distributed clients to learn a shared model for prediction while keeping the training data local on each client.
1 code implementation • 15 Sep 2021 • Xinrong Hu, Dewen Zeng, Xiaowei Xu, Yiyu Shi
With different amounts of labeled data, our methods consistently outperform the state-of-the-art contrast-based methods and other semi-supervised learning techniques.
no code implementations • 14 Sep 2021 • Dewen Zeng, Yukun Ding, Haiyun Yuan, Meiping Huang, Xiaowei Xu, Jian Zhuang, Jingtong Hu, Yiyu Shi
At the data acquisition time, the operator could not know the quality of the segmentation results.
Hardware Aware Neural Architecture Search Neural Architecture Search +1
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.
1 code implementation • 7 Sep 2021 • Dewen Zeng, John N. Kheir, Peng Zeng, Yiyu Shi
In this work, we use lung segmentation in chest X-rays as a case study and propose a contrastive learning framework with temporal correlated medical images, named CL-TCI, to learn superior encoders for initializing the segmentation network.
no code implementations • 1 Sep 2021 • Zeyang Yao, Jiawei Zhang, Hailong Qiu, Tianchen Wang, Yiyu Shi, Jian Zhuang, Yuhao Dong, Meiping Huang, Xiaowei Xu
Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation.
no code implementations • 6 Jul 2021 • Zheyu Yan, Da-Cheng Juan, Xiaobo Sharon Hu, Yiyu Shi
Emerging device-based Computing-in-memory (CiM) has been proved to be a promising candidate for high-energy efficiency deep neural network (DNN) computations.
2 code implementations • 29 Jun 2021 • Dewen Zeng, Mingqi Li, Yukun Ding, Xiaowei Xu, Qiu Xie, Ruixue Xu, Hongwen Fei, Meiping Huang, Jian Zhuang, Yiyu Shi
Experiment results on our clinical MCE data set demonstrate that the neural network trained with the proposed loss function outperforms those existing ones that try to obtain a unique ground truth from multiple annotations, both quantitatively and qualitatively.
1 code implementation • 16 Jun 2021 • Dewen Zeng, Yawen Wu, Xinrong Hu, Xiaowei Xu, Haiyun Yuan, Meiping Huang, Jian Zhuang, Jingtong Hu, Yiyu Shi
The success of deep learning heavily depends on the availability of large labeled training sets.
no code implementations • 7 Jun 2021 • Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu
After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy.
no code implementations • 18 May 2021 • Tianchen Wang, Zhihe Li, Meiping Huang, Jian Zhuang, Shanshan Bi, Jiawei Zhang, Yiyu Shi, Hongwen Fei, Xiaowei Xu
For PFO diagnosis, contrast transthoracic echocardiography (cTTE) is preferred as being a more robust method compared with others.
no code implementations • 25 Apr 2021 • Wentao Chen, Hailong Qiu, Jian Zhuang, Chutong Zhang, Yu Hu, Qing Lu, Tianchen Wang, Yiyu Shi, Meiping Huang, Xiaowe Xu
Deep neural networks (DNNs) have demonstrated their great potential in recent years, exceeding the per-formance of human experts in a wide range of applications.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 25 Apr 2021 • Xiaowe Xu, Jiawei Zhang, Jinglan Liu, Yukun Ding, Tianchen Wang, Hailong Qiu, Haiyun Yuan, Jian Zhuang, Wen Xie, Yuhao Dong, Qianjun Jia, Meiping Huang, Yiyu Shi
As one of the most commonly ordered imaging tests, computed tomography (CT) scan comes with inevitable radiation exposure that increases the cancer risk to patients.
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).
1 code implementation • 26 Jan 2021 • Xiaowei Xu, Tianchen Wang, Jian Zhuang, Haiyun Yuan, Meiping Huang, Jianzheng Cen, Qianjun Jia, Yuhao Dong, Yiyu Shi
To demonstrate this, we further present a baseline framework for the automatic classification of CHD, based on a state-of-the-art CHD segmentation method.
no code implementations • 1 Jan 2021 • Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, Jingtong Hu
In this paper, we propose a framework to automatically select the most representative data from unlabeled input stream on-the-fly, which only requires the use of a small data buffer for dynamic learning.
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.
no code implementations • 1 Jan 2021 • Xinrong Hu, long wen, shushui wang, Dongpo Liang, Jian Zhuang, Yiyu Shi
Though the training data is only labeled to supervise theclassifier, the segmenter and the classifier are trained in an end-to-end manner sothat optimizing classification performance also adjusts how the abnormal beats aresegmented.
no code implementations • 29 Dec 2020 • Yutian Chen, Xiaowei Xu, Dewen Zeng, Yiyu Shi, Haiyun Yuan, Jian Zhuang, Yuhao Dong, Qianjun Jia, Meiping Huang
Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial segmentation of Magnetic Resonance Imaging (MRI) sequences.
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 • 4 Nov 2020 • Shao-Cheng Wen, Yu-Jen Chen, Zihao Liu, Wujie Wen, Xiaowei Xu, Yiyu Shi, Tsung-Yi Ho, Qianjun Jia, Meiping Huang, Jian Zhuang
Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc.
no code implementations • 18 Aug 2020 • Zhenge Jia, Zhepeng Wang, Feng Hong, Lichuan Ping, Yiyu Shi, Jingtong Hu
We equip the system with real-time inference on both intracardiac and surface rhythm monitors.
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.
no code implementations • 18 Jul 2020 • Tianchen Wang, Xiaowei Xu, JinJun Xiong, Qianjun Jia, Haiyun Yuan, Meiping Huang, Jian Zhuang, Yiyu Shi
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions.
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 • Shunjie Dong, Jinlong Zhao, Maojun Zhang, Zhengxue Shi, Jianing Deng, Yiyu Shi, Mei Tian, Cheng Zhuo
In this paper, we propose a novel Deformable U-Net (DeU-Net) to fully exploit spatio-temporal information from 3D cardiac MRI video, including a Temporal Deformable Aggregation Module (TDAM) and a Deformable Global Position Attention (DGPA) network.
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.
no code implementations • 7 Jul 2020 • Yawen Wu, Zhepeng Wang, Yiyu Shi, Jingtong Hu
For example, when training ResNet-110 on CIFAR-10, we achieve 68% computation saving while preserving full accuracy and 75% computation saving with a marginal accuracy loss of 1. 3%.
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 • 23 Apr 2020 • Yawen Wu, Zhepeng Wang, Zhenge Jia, Yiyu Shi, Jingtong Hu
This work aims to enable persistent, event-driven sensing and decision capabilities for energy-harvesting (EH)-powered devices by deploying lightweight DNNs onto EH-powered devices.
no code implementations • CVPR 2020 • Song Bian, Tianchen Wang, Masayuki Hiromoto, Yiyu Shi, Takashi Sato
In this work, we propose ENSEI, a secure inference (SI) framework based on the frequency-domain secure convolution (FDSC) protocol for the efficient execution of privacy-preserving visual recognition.
no code implementations • 27 Feb 2020 • Jinglan Liu, Yukun Ding, JinJun Xiong, Qianjun Jia, Meiping Huang, Jian Zhuang, Bike Xie, Chun-Chen Liu, Yiyu Shi
For example, if the noise is large leading to significant difference between domain $X$ and domain $Y$, can we bridge $X$ and $Y$ with an intermediate domain $Z$ such that both the denoising process between $X$ and $Z$ and that between $Z$ and $Y$ are easier to learn?
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 • MIDL 2019 • Yukun Ding, Jinglan Liu, Xiaowei Xu, Meiping Huang, Jian Zhuang, JinJun Xiong, Yiyu Shi
Existing selective segmentation methods, however, ignore this unique property of selective segmentation and train their DNN models by optimizing accuracy on the entire dataset.
no code implementations • WS 2019 • Qingkai Zeng, Mengxia Yu, Wenhao Yu, JinJun Xiong, Yiyu Shi, Meng Jiang
On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts.
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.
no code implementations • 15 Sep 2019 • Tianchen Wang, JinJun Xiong, Xiaowei Xu, Meng Jiang, Yiyu Shi, Haiyun Yuan, Meiping Huang, Jian Zhuang
Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention.
1 code implementation • 10 Sep 2019 • Zheyu Yan, Yiyu Shi, Wang Liao, Masanori Hashimoto, Xichuan Zhou, Cheng Zhuo
We are then able to analytically explore the weakness of a network and summarize the key findings for the impact of SIPP on different types of bits in a floating point parameter, layer-wise robustness within the same network and impact of network depth.
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 • 6 Jul 2019 • Xiaowei Xu, Tianchen Wang, Dewen Zeng, Yiyu Shi, Qianjun Jia, Haiyun Yuan, Meiping Huang, Jian Zhuang
3D printing has been widely adopted for clinical decision making and interventional planning of Congenital heart disease (CHD), while whole heart and great vessel segmentation is the most significant but time-consuming step in the model generation for 3D printing.
1 code implementation • 31 May 2019 • Yuan Gong, Boyang Li, Christian Poellabauer, Yiyu Shi
In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail.
no code implementations • 15 Apr 2019 • Boyang Li, Changhao Chenli, Xiaowei Xu, Yiyu Shi, Taeho Jung
In this paper, we propose DLBC to exploit the computation power of miners for deep learning training as proof of useful work instead of calculating hash values.
no code implementations • CVPR 2019 • Zihao Liu, Xiaowei Xu, Tao Liu, Qi Liu, Yanzhi Wang, Yiyu Shi, Wujie Wen, Meiping Huang, Haiyun Yuan, Jian Zhuang
In this paper we will use deep learning based medical image segmentation as a vehicle and demonstrate that interestingly, machine and human view the compression quality differently.
no code implementations • 15 Mar 2019 • Tianchen Wang, JinJun Xiong, Xiaowei Xu, Yiyu Shi
By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models.
no code implementations • 7 Mar 2019 • Yukun Ding, Yiyu Shi
In coal-fired power plants, it is critical to improve the operational efficiency of boilers for sustainability.
1 code implementation • 5 Mar 2019 • Yukun Ding, Jinglan Liu, JinJun Xiong, Yiyu Shi
Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing accurate uncertainty estimation on many tasks, such as security cameras and autonomous driving vehicles.
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.
no code implementations • 1 Sep 2018 • Xiaowei Xu, Xinyi Zhang, Bei Yu, X. Sharon Hu, Christopher Rowen, Jingtong Hu, Yiyu Shi
The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018.
no code implementations • CVPR 2018 • Xiaowei Xu, Qing Lu, Yu Hu, Lin Yang, Sharon Hu, Danny Chen, Yiyu Shi
Unlike existing litera- ture on quantization which primarily targets memory and computation complexity reduction, we apply quan- tization as a method to reduce over tting in FCNs for better accuracy.
no code implementations • 26 Feb 2018 • Jinglan Liu, Jiaxin Zhang, Yukun Ding, Xiaowei Xu, Meng Jiang, Yiyu Shi
This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction.
no code implementations • ICLR 2019 • Yukun Ding, Jinglan Liu, JinJun Xiong, Yiyu Shi
To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.