Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Barukh Ziv"'
Autor:
Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem Adelman, Deepti Aggarwal, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford, Barukh Ziv, Alexander Heinecke
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 8 (2022)
During the past decade, novel Deep Learning (DL) algorithms, workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload and hardware ecosystems, the programming methodology of DL systems is stagnan
Externí odkaz:
https://doaj.org/article/0ca258dcbf0f493b97c2c2aef3829f43
Autor:
Narendra Chaudhary, Sanchit Misra, Dhiraj Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul
Publikováno v:
2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).
Autor:
Menachem Adelman, Narendra Chaudhary, Dhiraj D. Kalamkar, Barukh Ziv, Bharat Kaul, Sanchit Misra, Alexander Heinecke, Evangelos Georganas
Identifying accessible chromatin regions is a fundamental problem in epigenomics with ATAC-seq being a commonly used assay. Exponential rise in single cell ATAC-seq experiments has made it critical to accelerate processing of ATAC-seq data. ATAC-seq
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a2881cf74d8f1fb3a0dc7ee5bae7313
https://doi.org/10.1101/2021.09.28.462099
https://doi.org/10.1101/2021.09.28.462099
Autor:
Kunal Banerjee, Alexander Heinecke, Evangelos Georganas, Dhiraj D. Kalamkar, Barukh Ziv, Cristina S. Anderson, Eden Segal
Publikováno v:
Supercomputing Frontiers and Innovations. 6
Recurrent neural network (RNN) models have been found to be well suited for processing temporal data. In this work, we present an optimized implementation of vanilla RNN cell and its two popular variants: LSTM and GRU for Intel Xeon architecture. Typ
Publikováno v:
Computer Aided Verification ISBN: 9783540677703
CAV
CAV
Our experience with semi-exhaustive verification shows a severe degradation in usability for the corner-case bugs, where the tuning effort becomes much higher and recovery from dead-ends is more and more difficult. Moreover, when there are no bugs at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc93e08da448ff45991a3357c51346b8
https://doi.org/10.1007/10722167_30
https://doi.org/10.1007/10722167_30