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	<id>https://wiki.extremist.software/index.php?action=history&amp;feed=atom&amp;title=Talk%3ADreamTeam%2FReading</id>
	<title>Talk:DreamTeam/Reading - Revision history</title>
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	<updated>2026-04-10T16:50:54Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>https://wiki.extremist.software/index.php?title=Talk:DreamTeam/Reading&amp;diff=56704&amp;oldid=prev</id>
		<title>192.195.83.130: recent additions dump</title>
		<link rel="alternate" type="text/html" href="https://wiki.extremist.software/index.php?title=Talk:DreamTeam/Reading&amp;diff=56704&amp;oldid=prev"/>
		<updated>2017-02-10T07:32:57Z</updated>

		<summary type="html">&lt;p&gt;recent additions dump&lt;/p&gt;
&lt;a href=&quot;https://wiki.extremist.software/index.php?title=Talk:DreamTeam/Reading&amp;amp;diff=56704&amp;amp;oldid=56580&quot;&gt;Show changes&lt;/a&gt;</summary>
		<author><name>192.195.83.130</name></author>
	</entry>
	<entry>
		<id>https://wiki.extremist.software/index.php?title=Talk:DreamTeam/Reading&amp;diff=56580&amp;oldid=prev</id>
		<title>Danf: new finds, mostly re fpga</title>
		<link rel="alternate" type="text/html" href="https://wiki.extremist.software/index.php?title=Talk:DreamTeam/Reading&amp;diff=56580&amp;oldid=prev"/>
		<updated>2017-02-03T08:26:34Z</updated>

		<summary type="html">&lt;p&gt;new finds, mostly re fpga&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;2 Feb 2017&lt;br /&gt;
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&lt;br /&gt;
http://www.ijsret.org/pdf/120399.pdf&lt;br /&gt;
&amp;quot;A Literature survey for Object Recognition using Neural Networks in FPGA&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://kar.kent.ac.uk/14766/1/FPGA_based_Lorrentz_Howells.pdf&lt;br /&gt;
&amp;quot;An FPGA based adaptive weightless Neural Network Hardware&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://infoteh.etf.unssa.rs.ba/zbornik/2016/radovi/KST-1/KST-1-15.pdf&lt;br /&gt;
&amp;quot;Analysis of Visible Light Communication System for Implementation in Sensor Networks&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://www.ccs.fau.edu/~fuchs/pub/Exp_brain_res_slav.pdf&lt;br /&gt;
&amp;quot;Anatomically constrained minimum variance beamforming applied to EEG&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://www.ijsr.net/archive/v5i3/NOV162166.pdf&lt;br /&gt;
&amp;quot;Based on Multi-FPGA Neuron Simulation Hardware Platform&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1611.03000v1&lt;br /&gt;
&amp;quot;Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1606.00094v2&lt;br /&gt;
&amp;quot;Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms&amp;quot;&lt;br /&gt;
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&lt;br /&gt;
https://arxiv.org/pdf/1609.09671v1&lt;br /&gt;
&amp;quot;Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1606.04884v1&lt;br /&gt;
&amp;quot;cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL&amp;quot;&lt;br /&gt;
&lt;br /&gt;
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https://arxiv.org/pdf/1511.07376v2&lt;br /&gt;
&amp;quot;CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1609.09296v1&lt;br /&gt;
&amp;quot;Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1511.06530v2&lt;br /&gt;
&amp;quot;Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1608.04363v2&lt;br /&gt;
&amp;quot;Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1611.05128v1&lt;br /&gt;
&amp;quot;Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://www.ijser.org/researchpaper/Digital-Hardware-Implementation-of-Artificial-Neural-Network-for-Signal-Processing.pdf&lt;br /&gt;
&amp;quot;Digital Hardware Implementation of Artificial Neural Network for Signal Processing&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1612.00694v1&lt;br /&gt;
&amp;quot;ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://ethesis.nitrkl.ac.in/4217/1/FPGA_implementation_of_artificial_neural_networks.pdf&lt;br /&gt;
&amp;quot;FPGA IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://lab.fs.uni-lj.si/lasin/wp/IMIT_files/neural/doc/Omondi2006.pdf&lt;br /&gt;
&amp;quot;FPGA Implementations of Neural Networks&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://vast.cs.ucla.edu/sites/default/files/publications/ASP-DAC2017-1352-11.pdf&lt;br /&gt;
&amp;quot;FPGA-based Accelerator for Long Short-Term Memory Recurrent Neural Networks&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.7533&amp;amp;rep=rep1&amp;amp;type=pdf&lt;br /&gt;
&amp;quot;FPGA-TARGETED NEURAL ARCHITECTURE FOR EMBEDDED ALERTNESS DETECTION&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://yann.lecun.com/exdb/publis/pdf/farabet-iscas-10.pdf&lt;br /&gt;
&amp;quot;Hardware Accelerated Convolutional Neural Networks for Synthetic Vision Systems&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://www.emo.org.tr/ekler/21eb0b827c09dd1_ek.pdf&lt;br /&gt;
&amp;quot;HARDWARE IMPLEMENTATION OF A FEEDFORWARD NEURAL NETWORK USING FPGAs&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://arxiv.org/pdf/1609.01287v1&lt;br /&gt;
&amp;quot;Holographic Entanglement Entropy&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://jestec.taylors.edu.my/Vol%206%20Issue%204%20August%2011/Vol_6_4_411_428_AL%20JAMMAS.pdf&lt;br /&gt;
&amp;quot;IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://www.nmr.mgh.harvard.edu/meg/pdfs/1993-Hamalainen-RMP.pdf&lt;br /&gt;
&amp;quot;Magnetoencephalography - theory, instrumentation, and applications to non-invasive studies of the working human brain&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1602.09046v1&lt;br /&gt;
&amp;quot;On Complex Valued Convolutional Neural Networks&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
http://arxiv.org/ftp/arxiv/papers/1201/1201.4617.pdf&lt;br /&gt;
&amp;quot;Photo-Thermal Neural Excitation by Extrinsic and Intrinsic Absorbers: A Temperature-Rate Model&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1611.02450v1&lt;br /&gt;
&amp;quot;PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1511.05552v4.pdf&lt;br /&gt;
&amp;quot;Recurrent Neural Networks Hardware Implementation on FPGA&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1605.06402v1&lt;br /&gt;
&amp;quot;Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1511.06306v2&lt;br /&gt;
&amp;quot;Robust Convolutional Neural Networks under Adversarial Noise&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1701.03400v2&lt;br /&gt;
&amp;quot;Scaling Binarized Neural Networks on Reconfigurable Logic&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://homes.cs.washington.edu/~luisceze/publications/snnap-hpca-2015.pdf&lt;br /&gt;
&amp;quot;SNNAP: Approximate Computing on Programmable SoCs via Neural Acceleration&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1406.4729v4&lt;br /&gt;
&amp;quot;Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1612.04052v1&lt;br /&gt;
&amp;quot;Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1701.00485v2&lt;br /&gt;
&amp;quot;Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1603.05201v2.pdf&lt;br /&gt;
&amp;quot;Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units&amp;quot;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
https://arxiv.org/pdf/1606.05487v1&lt;br /&gt;
&amp;quot;YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights&amp;quot;&lt;/div&gt;</summary>
		<author><name>Danf</name></author>
	</entry>
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