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Patrick Judd
Researcher at University of Toronto
Publications - 25
Citations - 2272
Patrick Judd is an academic researcher from University of Toronto. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 16, co-authored 25 publications receiving 1772 citations.
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Journal ArticleDOI
Cnvlutin: ineffectual-neuron-free deep neural network computing
Jorge Albericio,Patrick Judd,Tayler Hetherington,Tor M. Aamodt,Natalie Enright Jerger,Andreas Moshovos +5 more
TL;DR: Cnvolutin (CNV), a value-based approach to hardware acceleration that eliminates most of these ineffectual operations, improving performance and energy over a state-of-the-art accelerator with no accuracy loss.
Journal ArticleDOI
Stripes: Bit-Serial Deep Neural Network Computing
TL;DR: Stripes (STR) relies on bit-serial compute units and on the parallelism that is naturally present within DNNs to improve performance and energy with no accuracy loss, and provides a new degree of adaptivity enabling on-the-fly trade-offs among accuracy, performance, and energy.
Proceedings ArticleDOI
Stripes: bit-serial deep neural network computing
TL;DR: This work presents STR, a hardware accelerator that uses bit-serial computations to improve energy efficiency and performance and its area and power overhead are estimated at 5 percent and 12 percent respectively.
Proceedings ArticleDOI
Bit-pragmatic deep neural network computing
Jorge Albericio,Alberto Delmas,Patrick Judd,Sayeh Sharify,Gerard OrLeary,Roman Genov,Andreas Moshovos +6 more
TL;DR: PRA as mentioned in this paper uses serial-parallel shift-and-add multiplication while skipping the zero bits of the serial input, eliminating most of the ineffectual computations on-the-fly.
Posted Content
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation.
TL;DR: This paper presents a workflow for 8-bit quantization that is able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are more difficult to quantize, such as MobileNets and BERT-large.