Search Results for author: An Zou

Found 6 papers, 0 papers with code

Chain of Compression: A Systematic Approach to Combinationally Compress Convolutional Neural Networks

no code implementations26 Mar 2024 Yingtao Shen, Minqing Sun, Jie Zhao, An Zou

Convolutional neural networks (CNNs) have achieved significant popularity, but their computational and memory intensity poses challenges for resource-constrained computing systems, particularly with the prerequisite of real-time performance.

Knowledge Distillation Model Compression +1

NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference

no code implementations23 May 2023 Ruiqi Sun, Siwei Ye, Jie Zhao, Xin He, Yiran Li, An Zou

The inherent diversity of computation types within individual Deep Neural Network (DNN) models imposes a corresponding need for a varied set of computation units within hardware processors.


Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference

no code implementations9 Jun 2022 Xiangjie Li, Chenfei Lou, Zhengping Zhu, Yuchi Chen, Yingtao Shen, Yehan Ma, An Zou

Predictive Exit can forecast where the network will exit (i. e., establish the number of remaining layers to finish the inference), which effectively reduces the network computation cost by exiting on time without running every pre-placed exiting layer.

Decision Making

RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks with Fine-Grain Utilization

no code implementations25 Jan 2021 An Zou, Jing Li, Christopher D. Gill, Xuan Zhang

In this paper, we propose RTGPU, which can schedule the execution of multiple GPU applications in real-time to meet hard deadlines.

Autonomous Vehicles Scheduling

Disjoining Pressure of Water in Nanochannels

no code implementations19 Oct 2020 An Zou, Sajag Poudel, Shalabh C. Maroo

Experiments of water wicking in 1D silicon-dioxide nanochannels of heights 59 nm, 87 nm, 124 nm and 1015 nm are used to estimate the disjoining pressure of water which was found to be as high as ~1. 5 MPa while exponentially decreasing with increasing channel height.

Fluid Dynamics Mesoscale and Nanoscale Physics Applied Physics

Droplet Evaporation on Porous Nanochannels for High Heat Flux Dissipation

no code implementations29 Sep 2020 Sajag Poudel, An Zou, Shalabh Chandra Maroo

The experimental findings are applied to evaluate the use of porous nanochannels geometry in spray cooling application, and is found to be capable of dissipating high heat fluxes upto ~77 W/cm2 at temperatures below nucleation, thus highlighting the thermal management potential of fabricated geometry.

Applied Physics Fluid Dynamics

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