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Judd, Patrick

  1. FP8 Formats for Deep Learning
    2022/09/12 by Paulius Micikevicius, Micikevicius, Paulius, Dusan Stosic +29 · 3 voices · 35 citations
    Computer Science · Engineering · #Advanced Neural Network Applications #CCD and CMOS Imaging Sensors #Neural Networks and Applications #cs.LG
  2. Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
    2020/04/20 by Hao Wu, Wu, Hao, Patrick Judd +7 · 31 citations
    Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  3. Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks
    2017/06/23 by Sayeh Sharify, Sharify, Sayeh, Alberto Delmás Lascorz +7 · 3 citations
    Computer Science · Engineering · #Advanced Memory and Neural Computing #Advanced Neural Network Applications #CCD and CMOS Imaging Sensors #Distributed #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Machine Learning (cs.LG) #Parallel #and Cluster Computing (cs.DC)
  4. Dynamic Stripes: Exploiting the Dynamic Precision Requirements of\n Activation Values in Neural Networks
    2017/06/01 by Alberto Delmás, Delmas, Alberto, Patrick Judd +5 · 2 citations
    Computer Science · Physics and Astronomy · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Parallel Computing and Optimization Techniques
  5. Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets
    2015/11/17 by Patrick Judd, Judd, Patrick, Jorge Albericio +11 · 1 citation
    Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Stochastic Gradient Optimization Techniques