PPT-ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars

Ali Shafiee Anirban Nag Naveen Muralimanohar Rajeev Balasubramonian John Paul Strachan Miao Hu R Stanley Williams Vivek Srikumar University of Utah . ABSTRACT From the desire to update the maximum road speed data for navigation devices a speed sign recognition and detection system is proposed This system should prevent accidental speeding at roads where the map data is incorrect for example due t Shekhar Mishra. 2. . Project-X. (Yousry Gohar. 1. , David Johnson. 2. , Todd Johnson. 2. ) . 1 Argonne National Laboratory. 2 Fermi National Accelerator Laboratory. Project-X: Nuclear Energy Application. North Side Walls and End Walls. 20-27 May 2015. Stephen Plate, Brookhaven National Laboratory. Anchoring the upper shield plate to the framework at the west end. 2. Installing one of four L-mounting brackets to the side walls at the west end.. Neural . Network Architectures:. f. rom . LeNet. to ResNet. Lana Lazebnik. Figure source: A. . Karpathy. What happened to my field?. . Classification:. . ImageNet. Challenge top-5 error. Figure source: . North Side Walls and End Walls. 20-27 May 2015. Stephen Plate, Brookhaven National Laboratory. Anchoring the upper shield plate to the framework at the west end. 2. Installing one of four L-mounting brackets to the side walls at the west end.. 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. BJ Furman. 21APR2016. DAC and ADC. Digital-to-Analog Conversion (DAC). Converts a binary . value to . a scaled ‘analog’ voltage. Used for controlling systems that require an analog input. . DC servo motor. and. Dr. Albert Einstein . 2. nd. and 8. th. period. ELAR. Books I read. I read “Isaac Newton, Organizing the Universe,”. By: William J. Boerst.. ©2004. . I also read “Albert Einstein, Giants of Science,”. 2015/10/02. 陳柏任. Outline. Neural Networks. Convolutional Neural Networks. Some famous CNN structure. Applications. Toolkit. Conclusion. Reference. 2. Outline. Neural Networks. Convolutional Neural Networks. Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . By, . . Sruthi. . Moola. Convolution. . Convolution is a common image processing technique that changes the intensities of a pixel to reflect the intensities of the surrounding pixels. A common use of convolution is to create image filters. Sergey Zagoruyko & Nikos Komodakis. Introduction. Comparing Patches across images is one of the most fundamental tasks in computer vision. Applications include structure from motion, wide baseline matching and building panorama. 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. José Ignacio Orlando. 1,2. , Elena Prokofyeva. 3,4. , Mariana del Fresno. 1,5. and Matthew B. Blaschko. 6. 1 . Instituto. . Pladema. , UNCPBA, . Tandil. , Argentina. 2. . Consejo. Nacional de . Investigaciones.