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Ashish Sirasao
Researcher at Xilinx
Publications - 33
Citations - 469
Ashish Sirasao is an academic researcher from Xilinx. The author has contributed to research in topics: Circuit design & Field-programmable gate array. The author has an hindex of 6, co-authored 30 publications receiving 282 citations.
Papers
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Proceedings ArticleDOI
MLPerf inference benchmark
Vijay Janapa Reddi,Christine Cheng,David Kanter,Peter Mattson,Guenther Schmuelling,Carole-Jean Wu,Brian M. Anderson,Maximilien Breughe,Mark Charlebois,William Chou,Ramesh Chukka,Cody Coleman,Sam Davis,Pan Deng,Greg Diamos,Jared Duke,Dave Fick,J. Scott Gardner,Itay Hubara,Sachin Satish Idgunji,Thomas B. Jablin,Jeff Jiao,Tom St. John,Pankaj Kanwar,David Lee,Jeffery Liao,Anton Lokhmotov,Francisco Massa,Peng Meng,Paulius Micikevicius,Colin Osborne,Gennady Pekhimenko,Arun Tejusve Raghunath Rajan,Dilip Sequeira,Ashish Sirasao,Fei Sun,Hanlin Tang,Michael Thomson,Frank Wei,Ephrem C. Wu,Lingjie Xu,Koichi Yamada,Bing Yu,George Yuan,Aaron Zhong,Peizhao Zhang,Yuchen Zhou +46 more
TL;DR: This paper presents the benchmarking method for evaluating ML inference systems, MLPerf Inference, and prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures.
Posted Content
MLPerf Inference Benchmark
Vijay Janapa Reddi,Christine Cheng,David Kanter,Peter Mattson,Guenther Schmuelling,Carole-Jean Wu,Brian M. Anderson,Maximilien Breughe,Mark Charlebois,William Chou,Ramesh Chukka,Cody Coleman,Sam Davis,Pan Deng,Greg Diamos,Jared Duke,Dave Fick,J. Scott Gardner,Itay Hubara,Sachin Satish Idgunji,Thomas B. Jablin,Jeff Jiao,Tom St. John,Pankaj Kanwar,David Lee,Jeffery Liao,Anton Lokhmotov,Francisco Massa,Peng Meng,Paulius Micikevicius,Colin Osborne,Gennady Pekhimenko,Arun Tejusve Raghunath Rajan,Dilip Sequeira,Ashish Sirasao,Fei Sun,Hanlin Tang,Michael Thomson,Frank Wei,Ephrem C. Wu,Lingjie Xu,Koichi Yamada,Bing Yu,George Yuan,Aaron Zhong,Peizhao Zhang,Yuchen Zhou +46 more
TL;DR: MLPerf Inference as mentioned in this paper is a benchmarking method for evaluating ML inference systems with different architectures and architectures. And it is based on the first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities.
Posted Content
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
Settle Sean,Manasa Bollavaram,Paolo D'Alberto,Elliott Delaye,Oscar Fernando C. Fernandez,Nicholas J. Fraser,Ng Aaron,Ashish Sirasao,Michael Wu +8 more
TL;DR: A quantization scheme that allows inferencing to be carried out using arithmetic that is fundamentally more efficient when compared to even half-precision floating-point, and achieves end-to-end post quantization accuracies comparable to the reference model.
Proceedings ArticleDOI
Deep learning challenges and solutions with xilinx FPGAs
TL;DR: The architectural, software, performance, and implementation challenges and solutions and current research on the use of programmable logic to enable deep learning applications are described.
Patent
Image preprocessing for generalized image processing
TL;DR: An example preprocessor circuit for formatting image data into a plurality of streams of image samples includes: a first buffer configured to store the image data and output a row of the plurality of rows as discussed by the authors.