Zobrazeno 1 - 10
of 283
pro vyhledávání: '"William J. Dally"'
Publikováno v:
IEEE Transactions on Industrial Electronics. 70:4751-4761
Autor:
Ben Keller, Rangharajan Venkatesan, Steve Dai, Stephen G. Tell, Brian Zimmer, Charbel Sakr, William J. Dally, C. Thomas Gray, Brucek Khailany
Publikováno v:
IEEE Journal of Solid-State Circuits. 58:1129-1141
Autor:
William J. Dally
Publikováno v:
GetMobile: Mobile Computing and Communications. 25:12-18
A mechanical ventilator keeps a patient with respiratory failure alive by pumping precisely controlled amounts of air (or an air/O2 mixture) at controlled pressure into the patient's lungs [3, 5]. During intake (inspiration), the ventilator meters th
Publikováno v:
IEEE Micro. 41:42-51
Graphics processing units (GPUs) power today’s fastest supercomputers, are the dominant platform for deep learning, and provide the intelligence for devices ranging from self-driving cars to robots and smart cameras. They also generate compelling p
Autor:
Peter M. Kogge, William J. Dally
Publikováno v:
2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS).
Autor:
Yoshinori Nishi, John W. Poulton, Walker J. Turner, Xi Chen, Sanquan Song, Brian Zimmer, Stephen G. Tell, Nikola Nedovic, John M. Wilson, William J. Dally, C. Thomas Gray
Publikováno v:
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits).
Autor:
Ben Keller, Rangharajan Venkatesan, Steve Dai, Stephen G. Tell, Brian Zimmer, William J. Dally, C. Thomas Gray, Brucek Khailany
Publikováno v:
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits).
Autor:
Steve Dai, Ben Keller, William J. Dally, Brucek Khailany, Rangharajan Venkatesan, Alicia Klinefelter, Robert M. Kirby, Saad Godil, Yanqing Zhang, Haoxing Ren, Bryan Catanzaro
Publikováno v:
IEEE Micro. 40:23-32
Recent advancements in machine learning provide an opportunity to transform chip design workflows. We review recent research applying techniques such as deep convolutional neural networks and graph-based neural networks in the areas of automatic desi
Publikováno v:
Genome biology and evolution, vol 14, iss 3
We present Champagne, a whole-genome method for generating character matrices for phylogenomic analysis using large genomic indel events. By rigorously picking orthologous genes and locating large insertion and deletion events, Champagne delivers a c
Publikováno v:
Communications of the ACM. 63:48-57
DSAs gain efficiency from specialization and performance from parallelism.