Ebrahim M. Songhori
37 Papers
56 Citations
Ebrahim M. Songhori is an academic researcher from Google. The author has contributed to research in topics: Computer science & Secure multi-party computation. The author has an hindex of 13, co-authored 32 publications. Previous affiliations of Ebrahim M. Songhori include Rice University & University of California, San Diego.
Chat about Author
Papers
A graph placement methodology for fast chip design
Azalia Mirhoseini,Anna Goldie,Anna Goldie,Mustafa Yazgan,Joe Wenjie Jiang,Ebrahim M. Songhori,Shen Wang,Young-Joon Lee,Eric Johnson,Omkar Pathak,Azade Nazi,Jiwoo Pak,Andy Tong,Kavya Srinivasa,William Hang,Emre Tuncer,Quoc V. Le,James Laudon,Richard Ho,Roger Carpenter,Jeffrey Dean +20 more
TL;DR: In this article, the authors presented a deep reinforcement learning approach to chip floorplanning, which can automatically generate chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area.
382
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
M. Sadegh Riazi,Christian Weinert,Oleksandr Tkachenko,Ebrahim M. Songhori,Thomas Schneider,Farinaz Koushanfar +5 more
- 29 May 2018
TL;DR: Chameleon as mentioned in this paper is a hybrid mixed protocol for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs, but does not support signed fixed-point numbers.
371
•Posted Content
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications.
M. Sadegh Riazi,Christian Weinert,Oleksandr Tkachenko,Ebrahim M. Songhori,Thomas Schneider,Farinaz Koushanfar +5 more
TL;DR: Chameleon as mentioned in this paper is a hybrid mixed protocol for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs, but does not support signed fixed-point numbers.
TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits
Ebrahim M. Songhori,Siam U. Hussain,Ahmad-Reza Sadeghi,Thomas Schneider,Farinaz Koushanfar +4 more
- 17 May 2015
TL;DR: Tiny Garble achieves an unprecedented level of compactness and scalability by using a sequential circuit description for GC, and is able to implement functions that have never been reported before, such as SHA-3.
•Posted Content
Chip Placement with Deep Reinforcement Learning
Azalia Mirhoseini,Anna Goldie,Mustafa Yazgan,Joe Jiang,Ebrahim M. Songhori,Shen Wang,Young-Joon Lee,Eric Johnson,Omkar Pathak,Sungmin Bae,Azade Nazi,Jiwoo Pak,Andy Tong,Kavya Srinivasa,William Hang,Emre Tuncer,Anand Babu,Quoc V. Le,James Laudon,C. Richard Ho,Roger Carpenter,Jeffrey Dean +21 more
TL;DR: This work presents a learning-based approach to chip placement, and shows that, in under 6 hours, this method can generate placements that are superhuman or comparable on modern accelerator netlists, whereas existing baselines require human experts in the loop and take several weeks.
231