PPT-New hardware architectures for efficient deep net processing

SCNN An Accelerator for Compressedsparse Convolutional Neural Networks 9 authors NVIDIA MIT Berkeley Stanford ISCA 2017 Convolution operation Reuse Memory size vs access energy Dataflow decides reuse. John Doyle, Caltech. Rethinking fundamentals*. Parameter estimation and goodness of fit measures for . fat tail. distributions. Hard limits . on robust, efficient networks integrating . comms. , controls, energy, materials. 2. Chapter 9 Objectives. Learn the properties that often distinguish RISC from CISC architectures.. Understand how multiprocessor architectures are classified.. Appreciate the factors that create complexity in multiprocessor systems.. OpenPOWER. . hardware into a heterogeneous infrastructure . Jobs Run on Mix Architecture While Users Get Coffee. Creating a mix architecture that incorporates the best new hardware without the users knowing . New-Generation Models & Methodology for Advancing . AI & SIP. Li Deng . Microsoft Research, Redmond, . USA. Tianjin University, July 2-5, 2013. (including joint work with colleagues at MSR, U of Toronto, etc.) . 2. Chapter 9 Objectives. Learn the properties that often distinguish RISC from CISC architectures.. Understand how multiprocessor architectures are classified.. Appreciate the factors that create complexity in multiprocessor systems.. Topic 3. 4/15/2014. Huy V. Nguyen. 1. outline. Deep learning overview. Deep v. shallow architectures. Representation learning. Breakthroughs. Learning principle: greedy layer-wise training. Tera. . scale: data, model, . CS295: Modern Systems: Application Case Study Neural Network Accelerator – 2 Sang-Woo Jun Spring 2019 Many slides adapted from Hyoukjun Kwon‘s Gatech “Designing CNN Accelerators ” and Fat tails, hard limits, thin layers John Doyle, Caltech Rethinking fundamentals* Parameter estimation and goodness of fit measures for fat tail distributions Hard limits on robust, efficient networks integrating Guri. . Sohi. University of Wisconsin-Madison. Celebrating Yale@75. September 19, 2014. Outline. Where have we come from . Where are we are likely going. 2. Where From: Hardware. Primary goal was performance. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand