9 citations · 12 across the 6 of their papers we have counts for
7 papers
A Fast Transformer-based General-Purpose Lossless Compressor
Yu Mao, Yufei Cui, Tei-Wei Kuo +1
Deep-learning-based compressor has received interests recently due to much improved compression ratio. However, modern approaches suffer from long execution time. To ease this prob…
Composite Resource Scheduling for Networked Control Systems
Peng Wu, Chenchen Fu, Tianyu Wang +4
Real-time end-to-end task scheduling in networked control systems (NCSs) requires the joint consideration of both network and computing resources to guarantee the desired quality o…
Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks
Tuan Hoang, Thanh-Toan Do, Tam V. Nguyen +1
This paper proposes two novel techniques to train deep convolutional neural networks with low bit-width weights and activations. First, to obtain low bit-width weights, most existi…
Low Overhead Online Data Flow Tracking for Intermittently Powered Non-volatile FPGAs
Xinyi Zhang, Clay Patterson, Yongpan Liu +3
Energy harvesting is an attractive way to power future IoT devices since it can eliminate the need for battery or power cables. However, harvested energy is intrinsically unstable.…
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex
Yufei Cui, Wuguannan Yao, Qiao Li +2
Estimating the predictive uncertainty of a Bayesian learning model is critical in various decision-making problems, e.g., reinforcement learning, detecting adversarial attack, self…
NetKernel: Making Network Stack Part of the Virtualized Infrastructure
Zhixiong Niu, Hong Xu, Peng Cheng +4
This paper presents a system called NetKernel that decouples the network stack from the guest virtual machine and offers it as an independent module. NetKernel represents a new par…
EasyConvPooling: Random Pooling with Easy Convolution for Accelerating Training and Testing
Jianzhong Sheng, Chuanbo Chen, Chenchen Fu +1
Convolution operations dominate the overall execution time of Convolutional Neural Networks (CNNs). This paper proposes an easy yet efficient technique for both Convolutional Neura…