PPT-Fast and Efficient Implementation of Convolutional Neural Networks on FPGA

Abhinav Podili Chi Zhang Viktor Prasanna Ming Hsieh Department of Electrical Engineering University of Southern California podili zhan527 prasanna uscedu fpgauscedu ASAP July 2017. 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 using Convolutional Neural Network and Simple Logistic Classifier. Hurieh. . Khalajzadeh. Mohammad . Mansouri. Mohammad . Teshnehlab. Table of Contents. Convolutional Neural . Networks. Proposed CNN structure for face recognition. Kong Da, Xueyu Lei & Paul McKay. Digit Recognition. Convolutional Neural Network. Inspired by the visual cortex. Our example: Handwritten digit recognition. Reference: . LeCun. et al. . Back propagation Applied to Handwritten Zip Code Recognition. 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.. 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. Deep Convolutional Neural Networks. Philipp Gysel. ECE Department. University of California, Davis. Laboratory for Embedded and Programmable Systems. Machine Vision: Past, Present and Future!. Feature Extraction Approaches. Convolutions. Reduce parameters. Capture shift-invariance: location of patch in image should not matter. Subsampling. Allows greater invariance to deformations. Allows the capture of large patterns with small filters. Generally a DAG, directed acyclic graph. VisGraph, HKUST. LeNet. AlexNet. ZF Net. GoogLeNet. VGGNet. ResNet. Learned convolutional filters: Stage 1. Visualizing and understanding convolutional neural networks.. Taras. . Mitran. Jeff Waller. HR Compensation Workflow. Scenario: ABC Corp wants to hire a statistician.. What the market rate for this job, at the 50. th. percentile? 60%ile?. Issue: Almost every company’s job title and description for roughly the same “job” is different than other companies.. Developing efficient deep neural networks. Forrest Iandola. 1. , Albert Shaw. 2. , Ravi Krishna. 3. , Kurt Keutzer. 4. 1. UC Berkeley → DeepScale → Tesla → Independent Researcher. 2. Georgia Tech → DeepScale → Tesla. Kannan . Neten. Dharan. Introduction . Alzheimer’s Disease is a kind of dementia which is caused by damage to nerve cells in the brain and the usual side effects of it are loss of memory or other cognitive impairments.. An overview and applications. Outline. Overview of Convolutional Neural Networks. The Convolution operation. A typical CNN model architecture. Properties of CNN models. Applications of CNN models. Notable CNN models.