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Concept

Fruit tree pruning

Related publications (5)

Structured and tiled-based pruning of Deep Learning models targeting FPGA implementations

Alexandre Schmid

Model compression techniques have lead to a reduction of size and number of computations of Deep Learning models. However, techniques such as pruning mostly lack of a real co-optimization with hardware platforms. For instance, implementing unstructured pru ...

IEEE2022

Progressive Correspondence Pruning by Consensus Learning

Mathieu Salzmann, Chen Zhao, Chenxu Zhao

Correspondence pruning aims to correctly remove false matches (outliers) from an initial set of putative correspondences. The pruning process is challenging since putative matches are typically extremely unbalanced, largely dominated by outliers, and the r ...

IEEE2021

Hardware-oriented pruning and quantization of Deep Learning models to detect life-threatening arrhythmias

Alexandre Schmid

Wearable solutions based on Deep Learning (DL) for real-time ECG monitoring are a promising alternative to detect life-threatening arrhythmias. However, DL models suffer of a large memory footprint, which hampers their adoption in portable technologies. Th ...

IEEE2021

A Fast Pruning Technique for Low-Power Inexact Circuit Design

Giovanni De Micheli, Pierre-Emmanuel Julien Marc Gaillardon, Luca Gaetano Amarù, Jaime Joven Murillo

Inexact Circuits are circuits in which the accuracy of the output can be traded for cost savings (energy, area and/or delay). In the context of advanced technology scaling and power density increase, inexact circuits appear to be very promising as ...

IEEE2015

Automatic Generation of Inexact Digital Circuits by Gate-level Pruning

Christian Enz, Vincent Frédéric Camus, Jérémy Lucien Maurice Schlachter

Inexact or approximate circuits show great ability to reduce power consumption at the cost of occasional errors in comparison to their conventional counterparts. Even though the benefits of such circuits have been proven for many applications, they are not ...

2015