PPT-Graph Neural Network(GNN) Inference on FPGA

Author : cheeserv | Published Date : 2020-10-22

CERN openlab Lightning Talks 15082019 Kazi Ahmed Asif Fuad Supervisor Sofia Vallecorsa GNN Inference on FPGA Kazi Ahmed Asif Fuad Project Background GNN Inference

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Graph Neural Network(GNN) Inference on FPGA: Transcript

CERN openlab Lightning Talks 15082019 Kazi Ahmed Asif Fuad Supervisor Sofia Vallecorsa GNN Inference on FPGA Kazi Ahmed Asif Fuad Project Background GNN Inference on FPGA Kazi Ahmed Asif Fuad. Daniel R. Schlegel. Department of Computer Science and Engineering. Problem Summary. Inference graphs. 2. in their current form only support propositional logic. We expand it to support . L. A. – A Logic of Arbitrary and Indefinite Objects.. What are Artificial Neural Networks (ANN)?. ". Colored. neural network" by Glosser.ca - Own work, Derivative of File:Artificial neural . network.svg. . Licensed under CC BY-SA 3.0 via Commons - https://commons.wikimedia.org/wiki/File:Colored_neural_network.svg#/media/File:Colored_neural_network.svg. CAP5615 Intro. to Neural Networks. Xingquan (Hill) Zhu. Outline. Multi-layer Neural Networks. Feedforward Neural Networks. FF NN model. Backpropogation (BP) Algorithm. BP rules derivation. Practical Issues of FFNN. Table of Contents. Part 1: The Motivation and History of Neural Networks. Part 2: Components of Artificial Neural Networks. Part 3: Particular Types of Neural Network Architectures. Part 4: Fundamentals on Learning and Training Samples. Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. Psychology 209 – Winter 2017. March 7, 2017. The DNC architecture. Key features of the architecture. Indefinite memory size (Turing). Accurate storage of long lists of items after a single presentation. Abhinav . Podili. , Chi Zhang, Viktor . Prasanna. Ming Hsieh Department of Electrical Engineering. University of Southern California. {. podili. , zhan527, . prasanna. }@usc.edu. fpga.usc.edu. ASAP, July 2017. 1. Table of contents. Recurrent models. Partially recurrent neural networks. . Elman networks. Jordan networks. Recurrent neural networks. BackPropagation Through Time. Dynamics of a neuron with feedback. Can incorporate Neural, Genetic and Fuzzy Components. Expert Systems can perform many functions. Rules can be fuzzy, quantum, modal, neural, Bayesian, etc.. Special inference methods may be used. Concepts of Knowledge Representation: . Dr. Abdul Basit. Lecture No. 1. Course . Contents. Introduction and Review. Learning Processes. Single & Multi-layer . Perceptrons. Radial Basis Function Networks. Support Vector and Committee Machines. Services in C#. Salvator Galea*, Nik Sultana*, Pietro Bressana†, David Greaves*,. Robert Soulé†, Andrew W. Moore*, Noa Zilberman* . *University of Cambridge, †Università della Svizzera italiana. Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA. January 2016. Input. : a simple directed graph G satisfying two rules:. 1. G is an oriented graph (no bi-directional connections), and. 2. every node (neuron) of G has at least one out-going edge.. Process. Mark Hasegawa-Johnson. April 6, 2020. License: CC-BY 4.0. You may remix or redistribute if you cite the source.. Outline. Why use more than one layer?. Biological inspiration. Representational power: the XOR function.

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