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Dynamic Hard Pruning of Neural Networks at the Edge of the Internet
[article]
2021
arXiv
pre-print
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DynHP enables a tunable size reduction of the final neural network and reduces the NN memory occupancy during training. ...
Neural Networks (NN), although successfully applied to several Artificial Intelligence tasks, are often unnecessarily over-parametrised. ...
Acknowledgements This work is partially supported by four projects: HumanE AI Network (EU H2020 HumanAI-Net, GA #952026) Big Data to Enable Global Disruption of the Grapevine-powered Industries (EU H2020 ...
arXiv:2011.08545v3
fatcat:bnauq7bcpzgwhka24grrycl2l4
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability
[article]
2021
arXiv
pre-print
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We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability. ...
We hope that our findings will inspire future efforts aimed at rigorously understanding optimization at the Edge of Stability. Code is available at https://github.com/locuslab/edge-of-stability. ...
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA, the National Science Foundation, or any ...
arXiv:2103.00065v2
fatcat:r32apl7rbrhp7bbupzq6wpowqm
Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks
2004
Neural Computation
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Hence, this result strongly supports conjectures that dynamical systems which are capable of doing complex computational tasks should operate near the edge of chaos, i.e. the transition from ordered to ...
We analyze how the type of dynamics (ordered or chaotic) exhibited by randomly connected networks of threshold gates driven by a time varying input signal depends on the parameters describing the distribution ...
Acknowledgments We would like to thank Alexander Kaske for stimulating discussions and helpful comments about the manuscript. ...
doi:10.1162/089976604323057443
pmid:15165396
fatcat:j6evksffgjaqnhshi7x2sp4qm4
Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge
[article]
2019
arXiv
pre-print
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networks for energy-efficient neural computing in IOT-based edge devices. ...
This has necessitated the search for efficient implementations of neural networks in terms of both computations and storage. ...
) program sponsored by DARPA, in part by the National Science Foundation, in part by Intel, in part by the ONR-MURI program and in part by the Vannevar Bush Faculty Fellowship. ...
arXiv:1902.00460v1
fatcat:jwm7igbuxnfslkiu4xe3nzueyq
FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things
[article]
2022
arXiv
pre-print
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edge of the network. ...
This paper presents FANN-on-MCU, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on ...
it trains the neural network. [39] . ...
arXiv:1911.03314v3
fatcat:ejoluwg6lzblzgncqv5pkwls4a
Random Neural Networks and Deep Learning for Attack Detection at the Edge
2019
2019 IEEE International Conference on Fog Computing (ICFC)
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Network-attack detection with random neural network We apply the random neural networks (RNN) [15] , [16] developed for deep learning recently [17] - [19] to detecting network attacks using network ...
INTRODUCTION With the proliferation of network attacks aiming at fraudulently accessing sensitive information or at rendering computer systems unreliable or unusable, cybersecurity has become one of the ...
doi:10.1109/icfc.2019.00009
dblp:conf/icfc/BrunY19
fatcat:ed4letmlvbe47mxxd2t2dw2ufq
sBSNN: Stochastic-Bits Enabled Binary Spiking Neural Network with On-Chip Learning for Energy Efficient Neuromorphic Computing at the Edge
[article]
2020
arXiv
pre-print
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In this work, we propose stochastic Binary Spiking Neural Network (sBSNN) composed of stochastic spiking neurons and binary synapses (stochastic only during training) that computes probabilistically with ...
We present an energy-efficient implementation of the proposed sBSNN using 'stochastic bit' as the core computational primitive to realize the stochastic neurons and synapses, which are fabricated in 90nm ...
Stochastic Binary Spiking Neural Network (sBSNN) The core building block of the proposed sBSNN is a set of input (pre) neurons connected to an output (post) neuron via binary weights. ...
arXiv:2002.11163v1
fatcat:c457skonkfauhg2zmht7ereuqy
Automatic deep heterogeneous quantization of Deep Neural Networks for ultra low-area, low-latency inference on the edge at particle colliders
[article]
2020
arXiv
pre-print
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This is crucial for the event selection procedure in proton-proton collisions at the CERN Large Hadron Collider, where resources are strictly limited and a latency of 𝒪(1) μs is required. ...
Here, we introduce a novel method for designing optimally heterogeneously quantized versions of deep neural network models for minimum-energy, high-accuracy, nanosecond inference and fully automated deployment ...
INTRODUCTION With edge computing, real-time inference of deep neural networks (DNNs) on custom hardware has become increasingly relevant. ...
arXiv:2006.10159v2
fatcat:7ccpczec5ja6jky73m24naqxeu
PULP-NN: A Computing Library for Quantized Neural Network inference at the edge on RISC-V Based Parallel Ultra Low Power Clusters
2019
2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
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in deep neural network inference. ...
at the maximum frequency. ...
ACKNOWLEDGEMENTS This work was supported in part the OPRECOMP (Open trans-PREcision COMPuting) project founded from the European Union's Horizon 2020 research and innovation program under Grant Agreement ...
doi:10.1109/icecs46596.2019.8965067
dblp:conf/icecsys/GarofaloR0RB19
fatcat:mabeu7k7vbaxlfw7kej66fibje
An Edge Detection Method for Grayscale Images based on BP Feedforward Neural Network
2013
International Journal of Computer Applications
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In this paper a Feedforward Neural Network (FNN) based algorithm is proposed to detect edges in gray scale images. The backpropagation learning algorithm is used to minimize the error. ...
Edge detection is a terminology in image processing that refers to algorithms which aim at identifying edges in an image. ...
Edge detection is a terminology in image processing that refers to algorithms which aim at identifying edges in an image. Many works have been done to detect the edges using neural networks. ...
doi:10.5120/11368-6627
fatcat:5rmxlen3kvacrpblcdlecaj25a
A Hybrid Method for Detection of Edges in Grayscale Images
2013
International Journal of Image Graphics and Signal Processing
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Edge detection is the most fundamental but at the same time most important task in image processing and analysis. ...
In the paper a hybrid approach combin ing Neural Network and Fu zzy log ic based edge detection algorithm is proposed to detect edges in grayscale images. ...
So it is a 2 layer feedforward network. Here 2 neurons at input layer corresponding to fuzzy values of standard deviation and gradient, 3 neurons at the hidden layer and 1 neuron at output layer. ...
doi:10.5815/ijigsp.2013.09.04
fatcat:thndiqmfsjalnjnhpsopu3z5gq
Image Edge Detection Algorithm Based on Fuzzy Radial Basis Neural Network
2021
Advances in Mathematical Physics
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The effect of image edge detection contour is finally selected as the 3 ∗ 3 Sobel operator for edge detection; the binarized image edge detection contour information is found as the minimum outer rectangle ...
The edge is the reflection of the main structure and contour of the image, and it is also the direct interpretation of image understanding and the basis for further segmentation and recognition. ...
Acknowledgments This work is supported by the National Key R&D Program of China (No. 2018YFC0809500) and the Key R&D Program of Yunnan Province (202003AC100001). ...
doi:10.1155/2021/4405657
fatcat:bo2pa5govvhfvle2qagasly2ke
Creating edge detectors by evolutionary reinforcement learning
2008
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
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A comparison between the evolved neural networks and two standard algorithms, the Sobel and Canny edge detectors, shows very good results. ...
The detector is constructed as a neural network that takes as input the pixel values from a given image region-the same way that standard edge detectors do. ...
Using Neural Networks for Edge Detection Pinho has applied artificial neural networks to edge detection as early as 1993 [10] . ...
doi:10.1109/cec.2008.4631278
dblp:conf/cec/SiebelGS08
fatcat:6bidbzs2ofezjftl4qvxp2motm
Interpreting Basis Path Set in Neural Networks
[article]
2019
arXiv
pre-print
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Moreover, we propose hierarchical algorithm HBPS to find basis path set B in fully connected neural network by decomposing the network into several independent and parallel substructures. ...
From the aspect of graph theory, this paper defines basis path, investigates structure properties of basis paths in regular fully connected neural network and interprets the graph representation of basis ...
Lemma 1 The basis path set must cover all edges of the neural network graph . Proof: Assume edge is not covered by the basis path set . ...
arXiv:1910.09402v1
fatcat:vevjtqfykzhavht2pv4zhzcpee
Neural network based edge detection for automated medical diagnosis
2011
2011 IEEE International Conference on Information and Automation
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The application of the proposed neural network approach to the edge detection of medical images for automated bladder cancer diagnosis is also investigated. ...
Fuzzy sets are introduced during the training phase to improve the generalization ability of neural networks. ...
Unlike the multiple neural network approaches proposed in [2] and [3] , only a single neural network is used for edge detection. ...
doi:10.1109/icinfa.2011.5949014
fatcat:435aqhoauvdzdkq7dxcydnn4f4
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