87 citations · 809 across the 93 of their papers we have counts for
16 papers · 1 filter
Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators
Weiwen Jiang, Qiuwen Lou, Zheyu Yan +4
Co-exploration of neural architectures and hardware design is promising to simultaneously optimize network accuracy and hardware efficiency. However, state-of-the-art neural archit…
On Neural Architecture Search for Resource-Constrained Hardware Platforms
Qing Lu, Weiwen Jiang, Xiaowei Xu +2
In the recent past, the success of Neural Architecture Search (NAS) has enabled researchers to broadly explore the design space using learning-based methods. Apart from finding bet…
MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation
Tianchen Wang, Jinjun Xiong, Xiaowei Xu +5
Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention. The MRI videos are expected to be segmented on-the-fly in real prac…
Privacy-Aware Cost-Effective Scheduling Considering Non-Schedulable Appliances in Smart Home
Boyang Li, Jie Wu, Yiyu Shi
A Smart Home provides integrating and electronic information services to help residential users manage their energy usage and bill cost but also exposes users to significant privac…
When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies
Zheyu Yan, Yiyu Shi, Wang Liao +3
Deep Neural Network has proved its potential in various perception tasks and hence become an appealing option for interpretation and data processing in security sensitive systems.…
Accurate Congenital Heart Disease Model Generation for 3D Printing
Xiaowei Xu, Tianchen Wang, Dewen Zeng +5
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 t…
Achieving Super-Linear Speedup across Multi-FPGA for Real-Time DNN Inference
Weiwen Jiang, Edwin H. -M. Sha, Xinyi Zhang +4
Real-time Deep Neural Network (DNN) inference with low-latency requirement has become increasingly important for numerous applications in both cloud computing (e.g., Apple's Siri)…
Hardware/Software Co-Exploration of Neural Architectures
Weiwen Jiang, Lei Yang, Edwin Sha +5
We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS). Different from existing hardware-aware NAS which assumes a fixed h…
Real-Time Adversarial Attacks
Yuan Gong, Boyang Li, Christian Poellabauer +1
In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on…
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
Zihao Liu, Xiaowei Xu, Tao Liu +7
Cloud based medical image analysis has become popular recently due to the high computation complexities of various deep neural network (DNN) based frameworks and the increasingly l…
DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services
Boyang Li, Changhao Chenli, Xiaowei Xu +2
With the increasing artificial intelligence application, deep neural network (DNN) has become an emerging task. However, to train a good deep learning model will suffer from enormo…
SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection
Tianchen Wang, Jinjun Xiong, Xiaowei Xu +1
Various convolutional neural networks (CNNs) were developed recently that achieved accuracy comparable with that of human beings in computer vision tasks such as image recognition,…
Real-Time Boiler Control Optimization with Machine Learning
Yukun Ding, Yiyu Shi
In coal-fired power plants, it is critical to improve the operational efficiency of boilers for sustainability. In this work, we formulate real-time boiler control as an optimizati…
Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off
Yukun Ding, Jinglan Liu, Jinjun Xiong +1
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 acc…
Energy-recycling Blockchain with Proof-of-Deep-Learning
Changhao Chenli, Boyang Li, Yiyu Shi +1
An enormous amount of energy is wasted in Proofof-Work (PoW) mechanisms adopted by popular blockchain applications (e.g., PoW-based cryptocurrencies), because miners must conduct a…
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search
Weiwen Jiang, Xinyi Zhang, Edwin H. -M. Sha +4
A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific dataset? Recently, several Neural Architec…