"Accelerating GPU Betweenness Centrality" by McLaughlin and Bader ably addresses the challenges to authors of efficient graph implementations in the important context...John D. Owens From Communications of the ACM | August 2018
We present a hybrid GPU implementation that provides good performance on graphs of arbitrary structure rather than just scale-free graphs as was done previously...Adam McLaughlin, David A. Bader From Communications of the ACM | August 2018
In "Coz: Finding Code that Counts with Causal Profiling," Curtsinger and Berger describe causal profiling, which tell programmers exactly how much speed-up bang...Landon P. Cox From Communications of the ACM | June 2018
This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts...Charlie Curtsinger, Emery D. Berger From Communications of the ACM | June 2018
"Exploiting the Analog Properties of Digital Circuits for Malicious Hardware," by Kaiyuan Yang, et al., assumes semiconductor foundries (and others in chip fabrication)...Charles (Chuck) Thacker From Communications of the ACM | September 2017
We show how a fabrication-time attacker can leverage analog circuits to create a hardware attack that is small and stealthy.
Kaiyuan Yang, Matthew Hicks, Qing Dong, Todd Austin, Dennis Sylvester From Communications of the ACM | September 2017
This paper introduces an interface-driven approach to building scalable software.
Austin T. Clements, M. Frans Kaashoek, Eddie Kohler, Robert T. Morris, Nickolai Zeldovich From Communications of the ACM | August 2017
"IronFleet: Proving Safety and Liveness of Practical Distributed Systems," by Chris Hawblitzel, et al., describes mechanically checked proofs for two non-trivial...Fred B. Schneider From Communications of the ACM | July 2017
We demonstrate the methodology on a complex implementation of a Paxos-based replicated state machine library and a lease-based sharded key-value store. With our...Chris Hawblitzel, Jon Howell, Manos Kapritsos, Jacob R. Lorch, Bryan Parno, Michael L. Roberts, Srinath Setty, Brian Zill From Communications of the ACM | July 2017
We are in the middle of the third wave of interest in artificial neural networks as the leading paradigm for machine learning. "ImageNet Classification with Deep...Jitendra Malik From Communications of the ACM | June 2017
In the 1980s backpropagation did not live up to the very high expectations of its advocates. Twenty years later, we know what went wrong: for deep neural networks...Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton From Communications of the ACM | June 2017
The authors of "Powering the Next Billion Devices with Wi-Fi" turn the problem of powering wireless sensor networks on its head. Instead of focusing on energy harvesting...Srinivasan Keshav From Communications of the ACM | March 2017
We present the first power over Wi-Fi system that delivers power to low-power sensors and devices and works with existing Wi-Fi chipsets.
Vamsi Talla, Bryce Kellogg, Benjamin Ransford, Saman Naderiparizi, Joshua R. Smith, Shyamnath Gollakota From Communications of the ACM | March 2017
"A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services" presents a research deployment of Field Programmable Gate Arrays (FPGAs) in a Microsoft...James C. Hoe From Communications of the ACM | November 2016
We describe a medium-scale deployment of a composable, reconfigurable hardware fabric on a bed of 1,632 servers, and measure its effectiveness in accelerating the...Andrew Putnam, Adrian M. Caulfield, Eric S. Chung, Derek Chiou, Kypros Constantinides, John Demme, Hadi Esmaeilzadeh, Jeremy Fowers, Gopi Prashanth Gopal, Jan Gray, Michael Haselman, Scott Hauck, Stephen Heil, Amir Hormati, Joo-Young Kim, Sitaram Lanka, James Larus, Eric Peterson, Simon Pope, Aaron Smith, Jason Thong, Phillip Yi Xiao, Doug Burger From Communications of the ACM | November 2016
"DianNao Family: Energy-Efficient Hardware Accelerators for Machine Learning" shows a deep understanding of both neural net implementations and the issues in computer...Kurt Keutzer From Communications of the ACM | November 2016
We introduce a series of hardware accelerators (i.e., the DianNao family) designed for Machine Learning (especially neural networks), with a special emphasis on...Yunji Chen, Tianshi Chen, Zhiwei Xu, Ninghui Sun, Olivier Temam From Communications of the ACM | November 2016
"Verifying Quantitative Reliability for Programs that Execute on Unreliable Hardware" by Carbin et al. addresses challenges related to a bug, how likely it is to...Todd Millstein From Communications of the ACM | August 2016
In "Probabilistic Theorem Proving," Gogate and Domingos suggest how PTP could be turned in a fast approximate algorithm by sampling from the set of children of...Henry Kautz, Parag Singla From Communications of the ACM | July 2016