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.. Final presentation. One semester – winter 2014/15. By : Dana Abergel and Alex . Fonariov. Supervisor : . Mony. . Orbach. High Speed Digital System Laboratory. Abstract . Matrix multiplication is a complex mathematical operation.. Part 1. Objectives. After completing this module, you will be able to:. Identify and differentiate the members of the 7 series families. 7 Series FPGA Families. Logic Cells. 20K – 355K. 70K – 480K. Tamás Herendi, S. Roland Major. UDT2012. Introduction. The presented work is . based on the algorithm by . T. Herendi . for constructing uniformly distributed linear recurring sequences to be used for pseudo-random number . Vaughn Betz. University of Toronto. With special thanks to . Mohamed . Abdelfattah. ,. Andrew . Bitar. . and Kevin Murray. Overview. Why do we need a new system-level interconnect?. Why an embedded . Seyi. . Ayorinde. Pooja. Paul . Chaudhury. FPGA. 2. Field Programmable Gate Array. Reconfigurable Circuit. Configurable Logic Blocks (CLBs). Calhoun et al.: Flexible Circuits and Architectures for Ultralow Power. ASICs. Application Specific . Integrated Circuits. Microprocessors. . Microcontrollers. FPGA Principles. A Field-Programmable Gate Array (FPGA) is an integrated circuit that can be configured by the user to emulate any digital circuit as long as there are enough resources. P14571. Altera FPGA’s. . . Logic Elements. ALM. Registers. M20K Memory. DSP Blocks. Multipliers. PLL. . . . . . Blocks. Mbits. . . FPGA. HPS. High End. Stratix V GX. 952. 0. 1,437,000. Few-Shot Learning with Graph Neural Networks CS 330 Paper Presentation Problem Image source: Ravi, Sachin, and Hugo Larochelle. “Optimization as a model for few-shot learning,” 2017, 11. Some approaches to few-shot learning: Paris, 2016-01-26. 2. Contents. Introduction . Brief review of ongoing IAC Adaptive Optics projects. Summary of control technologies used . Technologies comparison . C. onclusions. 3. Contents. Introduction. Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA. Gsensor. to LED. Prelab Activities:. Complete the homework given for Lab 6. Go Through the training “DE0-Nano-SoC_My_First_HPS_FPGA.pdf” from the Lab manual. Learn how to use . Qsys. tool and design system with Bridges connecting HPS and NIOS II processors. Callie Hao. Assistant Professor. ECE, Georgia Institute of Technology. Sharc-lab @ Georgia Tech . https://sharclab.ece.gatech.edu/. Background. Graph Neural Network (GNN). Reinforcement Learning (RL). Code. /. Design. . R. eview. CERN, . 2011-. 0. 5-05. Aleš Svetek. J. Stefan Institute, Ljubljana. Agenda. BCM . FPGA . M. ain . T. asks. Upgrade v3 . . . v4. BCM FPGA Data . Flow. BCM FPGA Firmware v4 .

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