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 . com Jimmy SJ Ren Lenovo Research Technology jimmysjrengmailcom Ce Liu Microsoft Research celiumicrosoftcom Jiaya Jia The Chinese University of Hong Kong leojiacsecuhkeduhk Abstract Many fundamental imagerelated problems involve deconvol ution operat 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. Ali Shafiee*, . Anirban Nag*. , Naveen Muralimanohar. †. , Rajeev Balasubramonian*, John Paul Strachan. †. , Miao Hu. †. , R. Stanley Williams. †. , Vivek Srikumar*. *University of Utah . 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. 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. 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. 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 . Convolutional Neural Networks. Spring 2018. CS 599.. Instructor: Jyo Deshmukh. Neural network basics. Convolutional Neural Nets. Layout. 2. A feedforward neural network with . hidden layers is defined as follows:. 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. 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.