Zobrazeno 1 - 10
of 309
pro vyhledávání: '"Rob A. Rutenbar"'
Autor:
Adel Ejjeh, Leon Medvinsky, Aaron Councilman, Hemang Nehra, Suraj Sharma, Vikram Adve, Luigi Nardi, Eriko Nurvitadhi, Rob A Rutenbar
Publikováno v:
2022 IEEE 33rd International Conference on Application-specific Systems, Architectures and Processors (ASAP).
Publikováno v:
FPGA
High Level Synthesis (HLS) tools, like the Intel FPGA SDK for OpenCL, improve design productivity and enable efficient design space exploration guided by simple program directives (pragmas), but may sometimes miss important optimizations necessary fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da7d89fce75b3efbf0f5db0180861b08
Autor:
Glenn G. Ko, Paul N. Whatmough, Rob A. Rutenbar, Yuji Chai, David Brooks, Marco Donato, Thierry Tambe, Gu-Yeon Wei
Publikováno v:
Hot Chips Symposium
Autor:
Marco Donato, Thierry Tambe, David Brooks, Gu-Yeon Wei, Rob A. Rutenbar, Yuji Chai, Glenn G. Ko, Paul N. Whatmough
Publikováno v:
VLSI Circuits
This paper describes a 16nm programmable accelerator for unsupervised probabilistic machine perception tasks that performs Bayesian inference on probabilistic models mapped onto a 2D Markov Random Field, using MCMC. Exploiting two degrees of parallel
Publikováno v:
FCCM
Homomorphic encryption (HE) is one of the most promising solutions to secure cloud computing. The number theoretic transform (NTT) that is widely used for convolution operations in HE requires a large amount of computation and has high parallelism, a
Publikováno v:
ReConFig
Homomorphic encryption (HE) is an important cryptographic primitive which allows privacy preserving computations. Current HE schemes are all based on modular arithmetic. Modular multiplication (ModMult) is one of the most frequently used modular oper
Autor:
Rob A. Rutenbar, Glenn G. Ko
Publikováno v:
ACM Journal on Emerging Technologies in Computing Systems. 14:1-22
Machine learning (ML) has revolutionized a wide range of recognition tasks, ranging from text analysis to speech to vision, most notably in cloud deployments. However, mobile deployment of these ideas involves a very different category of design prob
Autor:
Brent Hailpern, Alexander S. Szalay, Henrik I. Christensen, Padma Raghavan, Deborah Estrin, Francine Berman, Victoria Stodden, Rob A. Rutenbar, Susan B. Davidson, Michael J. Franklin, Margaret Martonosi
Publikováno v:
Communications of the ACM. 61:67-72
Data science promises new insights, helping transform information into knowledge that can drive science and industry.
Publikováno v:
FPL
Bayesian models and inference is a class of machine learning that is useful for solving problems where the amount of data is scarce and prior knowledge about the application allows you to draw better conclusions. However, Bayesian models often requir
Autor:
Tianqi Gao, Rob A. Rutenbar
Publikováno v:
ASAP
Graph Cuts is a popular technique for Maximum A Posteriori inference in computer vision. It transforms a Markov Random Field problem into a network flow problem, solved via the Push-Relabel algorithm. While attractively simple, the large size of a ty