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Shweta Jain
Researcher at Intel
Publications - 7
Citations - 2331
Shweta Jain is an academic researcher from Intel. The author has contributed to research in topics: Computer science & Spiking neural network. The author has an hindex of 1, co-authored 1 publications receiving 1295 citations.
Papers
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Journal ArticleDOI
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Michael Davies,Narayan Srinivasa,Tsung-Han Lin,Gautham N. Chinya,Cao Yongqiang,Sri Harsha Choday,Georgios D. Dimou,Prasad Joshi,Nabil Imam,Shweta Jain,Yuyun Liao,Chit-Kwan Lin,Andrew Lines,Ruokun Liu,Deepak A. Mathaikutty,Steven McCoy,Arnab Paul,Jonathan Tse,Guruguhanathan Venkataramanan,Yi-Hsin Weng,Andreas Wild,Yoon Seok Yang,Hong Wang +22 more
TL;DR: Loihi is a 60-mm2 chip fabricated in Intels 14-nm process that advances the state-of-the-art modeling of spiking neural networks in silicon, and can solve LASSO optimization problems with over three orders of magnitude superior energy-delay-product compared to conventional solvers running on a CPU iso-process/voltage/area.
Proceedings ArticleDOI
Distributed Hardware Accelerated Secure Joint Computation on the COPA Framework
Rushi Patel,Pouya Haghi,Shweta Jain,Andriy Kot,Venkata Krishnan,Mayank Varia,Martin C. Herbordt +6 more
TL;DR: It is shown that utilizing the COPA framework enables multiple MPC accelerators running in parallel to fully saturate a 100Gbps link enabling higher performance compared to traditional NICs.
Proceedings ArticleDOI
Quantum Machine Learning and Quantum Communication Networks: The 2030s and the Future
TL;DR: In this paper , the potential of quantum principles and its peculiarities is employed with machine learning, and quantum machine learning reaches a very advanced level, which can effectively address a wide range of real-world issues.
Book ChapterDOI
YACC: A Framework Generalizing TuránShadow for Counting Large Cliques
Shweta Jain,Hanghang Tong +1 more
TL;DR: In this article , the authors revisited the TuránShadow algorithm and proposed a generalized framework called YACC that leverages several insights about real-world graphs to achieve faster clique-counting.
Journal ArticleDOI
Affective Computational Advertising Based on Perceptual Metrics
TL;DR: ACAD as discussed by the authors is an ad vertising framework expressly derived from percep-tual metrics, which incorporates findings from a user study examining the effect of within-program ad placements on ad perception.