N
Negar Neda
Publications - 4
Citations - 3
Negar Neda is an academic researcher. The author has contributed to research in topics: Computer science & Multiplier (economics). The author has an hindex of 1, co-authored 4 publications receiving 3 citations.
Papers
More filters
Proceedings ArticleDOI
Multi-Precision Deep Neural Network Acceleration on FPGAs
TL;DR: Experimental results show that by enabling run-time precision adjustment, mpDNN can offer 3-15x improvement in throughput, effectively increasing throughput when lower precision is required.
Proceedings ArticleDOI
RPU: The Ring Processing Unit
Deepraj Soni,Negar Neda,Naifeng Zhang,Benedict J. Reynwar,Homer Gamil,Benjamin C. Heyman,Mohammed Nabeel,Ahmad Al Badawi,Yuriy Polyakov,Massoud Pedram,Michail Maniatakos,David Bruce Cousins,Franz Franchetti,Matthew French,B. Schmidt,Brandon Reagen +15 more
TL;DR: In this paper , the ring processing unit (RPU) is proposed to accelerate ring-based computations of homomorphic encryption and post-quantum cryptography using vector Instruction Set Architecture (ISA) and micro-architecture.
Journal ArticleDOI
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation
David Bruce Cousins,Yuriy Polyakov,Ahmad Al Badawi,Matthew French,B. Schmidt,Ajey P. Jacob,Benedict J. Reynwar,Akhilesh Jaiswal,Clynn Mathew,Homer Gamil,Negar Neda,Deepraj Soni,Michail Maniatakos,Brandon Reagen,Naifeng Zhang,Franz Franchetti,Patrick Brinich,Michael Franusich,B. Y. Zhang,Zeming Cheng,Massoud Pedram +20 more
TL;DR: TREBUCHET as discussed by the authors is a tile-based chip with highly parallel ALUs optimized for vectorized 128b modulo arithmetic for fully homomorphic encryption (FHE).
Peer ReviewDOI
Towards Fast and Scalable Private Inference
TL;DR: Huang et al. as discussed by the authors reviewed recent efforts on addressing various PPC overheads using private inference (PI) in neural network as a motivating application, and two solutions are presented: HAAC for accelerating GCs and RPU for accelerating HE.