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Found 6 papers, 0 papers with code
Chain of Compression: A Systematic Approach to Combinationally Compress Convolutional Neural Networks
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.
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference
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
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.
RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks with Fine-Grain Utilization
In this paper, we propose RTGPU, which can schedule the execution of multiple GPU applications in real-time to meet hard deadlines.
Disjoining Pressure of Water in Nanochannels
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
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
