PPT-ISAAC: A Convolutional Neural Network Accelerator with In-S

Ali Shafiee Anirban Nag Naveen Muralimanohar Rajeev Balasubramonian John Paul Strachan Miao Hu R Stanley Williams Vivek Srikumar University of Utah . RECOGNITION. does size matter?. Karen . Simonyan. Andrew . Zisserman. Contents. Why I Care. Introduction. Convolutional Configuration . Classification. Experiments. Conclusion. Big Picture. Why I . care. 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. 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: . 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. 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. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. Munif. CNN. The (CNN. ) . consists of: . . Convolutional layers. Subsampling Layers. Fully . connected . layers. Has achieved state-of-the-art result for the recognition of handwritten digits. Neural . Isaac, Betsey, and Rosa. Wilson, Charley, Rebecca, and Rosa. Wilson Chinn. Isaac and Rosa. Isaac, Betsey, and Rosa. Wilson, Charley, Rebecca, and Rosa. Prabhas. . Chongstitvatana. Faculty of Engineering. Chulalongkorn. university. More Information. Search “Prabhas Chongstitvatana”. Go to me homepage. Perceptron. Rosenblatt, 1950. Multi-layer perceptron. 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. n,k. ) code by adding the r parity digits. An alternative scheme that groups the data stream into much smaller blocks k digits and encode them into n digits with order of k say 1, 2 or 3 digits at most is the convolutional codes. Such code structure can be realized using convolutional structure for the data digits.. 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.