no code implementations • 22 May 2024 • Benjamin C. Lee, David Brooks, Arthur van Benthem, Udit Gupta, Gage Hills, Vincent Liu, Benjamin Pierce, Christopher Stewart, Emma Strubell, Gu-Yeon Wei, Adam Wierman, Yuan YAO, Minlan Yu
For embodied carbon, we must re-think conventional design strategies -- over-provisioned monolithic servers, frequent hardware refresh cycles, custom silicon -- and adopt life-cycle design strategies that more effectively reduce, reuse and recycle hardware at scale.
no code implementations • 5 May 2024 • Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
Training large-scale machine learning models poses distinct system challenges, given both the size and complexity of today's workloads.
no code implementations • 22 Dec 2023 • Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal information presents unique challenges to quality, performance, and efficiency.
1 code implementation • NeurIPS 2023 • Syed Talal Wasim, Kabila Haile Soboka, Abdulrahman Mahmoud, Salman Khan, David Brooks, Gu-Yeon Wei
This paper presents a novel method to enhance the reliability of image classification models during deployment in the face of transient hardware errors.
no code implementations • 4 Oct 2023 • Samuel Hsia, Alicia Golden, Bilge Acun, Newsha Ardalani, Zachary DeVito, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
Training and deploying large machine learning (ML) models is time-consuming and requires significant distributed computing infrastructures.
no code implementations • 25 Sep 2023 • Celine Lee, Abdulrahman Mahmoud, Michal Kurek, Simone Campanoni, David Brooks, Stephen Chong, Gu-Yeon Wei, Alexander M. Rush
In this work, we leverage the strengths of LMs and symbolic solvers in a neurosymbolic approach to learned transpilation for assembly code.
1 code implementation • 4 Jul 2023 • Mehdi Rezaie, Ashley J. Ross, Hee-Jong Seo, Hui Kong, Anna Porredon, Lado Samushia, Edmond Chaussidon, Alex Krolewski, Arnaud de Mattia, Florian Beutler, Jessica Nicole Aguilar, Steven Ahlen, Shadab Alam, Santiago Avila, Benedict Bahr-Kalus, Jose Bermejo-Climent, David Brooks, Todd Claybaugh, Shaun Cole, Kyle Dawson, Axel de la Macorra, Peter Doel, Andreu Font-Ribera, Jaime E. Forero-Romero, Satya Gontcho A Gontcho, Julien Guy, Klaus Honscheid, Theodore Kisner, Martin Landriau, Michael Levi, Marc Manera, Aaron Meisner, Ramon Miquel, Eva-Maria Mueller, Adam Myers, Jeffrey A. Newman, Jundan Nie, Nathalie Palanque-Delabrouille, Will Percival, Claire Poppett, Graziano Rossi, Eusebio Sanchez, Michael Schubnell, Gregory Tarlé, Benjamin Alan Weaver, Christophe Yèche, Zhimin Zhou, Hu Zou
We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter fNL.
1 code implementation • 13 Jun 2023 • Yuji Chai, John Gkountouras, Glenn G. Ko, David Brooks, Gu-Yeon Wei
We introduce a method that dramatically reduces fine-tuning VRAM requirements and rectifies quantization errors in quantized Large Language Models.
no code implementations • 9 Jun 2023 • Yunho Jin, Chun-Feng Wu, David Brooks, Gu-Yeon Wei
Generating texts with a large language model (LLM) consumes massive amounts of memory.
no code implementations • 4 May 2023 • Sai Qian Zhang, Thierry Tambe, Nestor Cuevas, Gu-Yeon Wei, David Brooks
To minimize the occurrence of expensive eDRAM refresh operations, it is beneficial to shorten the lifetime of stored data during the training process.
no code implementations • 21 Feb 2023 • Samuel Hsia, Udit Gupta, Bilge Acun, Newsha Ardalani, Pan Zhong, Gu-Yeon Wei, David Brooks, Carole-Jean Wu
Based on our characterization of various embedding representations, we propose a hybrid embedding representation that achieves higher quality embeddings at the cost of increased memory and compute requirements.
no code implementations • 26 Jan 2023 • Yuji Chai, Devashree Tripathy, Chuteng Zhou, Dibakar Gope, Igor Fedorov, Ramon Matas, David Brooks, Gu-Yeon Wei, Paul Whatmough
The ability to accurately predict deep neural network (DNN) inference performance metrics, such as latency, power, and memory footprint, for an arbitrary DNN on a target hardware platform is essential to the design of DNN based models.
1 code implementation • 26 Jan 2023 • Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Yang Li, Liangzhen Lai, Ilias Leontiadis, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
no code implementations • 1 Dec 2022 • Matthew Adiletta, David Brooks, Gu-Yeon Wei
Graph Neural Networks (GNNs) are a class of neural networks designed to extract information from the graphical structure of data.
no code implementations • 25 Sep 2022 • Yuji Chai, Luke Bailey, Yunho Jin, Matthew Karle, Glenn G. Ko, David Brooks, Gu-Yeon Wei, H. T. Kung
While research in the field of transformer models has primarily focused on enhancing performance metrics such as accuracy and perplexity, practical applications in industry often necessitate a rigorous consideration of inference latency constraints.
no code implementations • 13 Mar 2022 • Bilal Shaikh, Lucian P. Smith, Dan Vasilescu, Gnaneswara Marupilla, Michael Wilson, Eran Agmon, Henry Agnew, Steven S. Andrews, Azraf Anwar, Moritz E. Beber, Frank T. Bergmann, David Brooks, Lutz Brusch, Laurence Calzone, Kiri Choi, Joshua Cooper, John Detloff, Brian Drawert, Michel Dumontier, G. Bard Ermentrout, James R. Faeder, Andrew P. Freiburger, Fabian Fröhlich, Akira Funahashi, Alan Garny, John H. Gennari, Padraig Gleeson, Anne Goelzer, Zachary Haiman, Joseph L. Hellerstein, Stefan Hoops, Jon C. Ison, Diego Jahn, Henry V. Jakubowski, Ryann Jordan, Matúš Kalaš, Matthias König, Wolfram Liebermeister, Synchon Mandal, Robert McDougal, J. Kyle Medley, Pedro Mendes, Robert Müller, Chris J. Myers, Aurelien Naldi, Tung V. N. Nguyen, David P. Nickerson, Brett G. Olivier, Drashti Patoliya, Loïc Paulevé, Linda R. Petzold, Ankita Priya, Anand K. Rampadarath, Johann M. Rohwer, Ali S. Saglam, Dilawar Singh, Ankur Sinha, Jacky Snoep, Hugh Sorby, Ryan Spangler, Jörn Starruß, Payton J. Thomas, David van Niekerk, Daniel Weindl, Fengkai Zhang, Anna Zhukova, Arthur P. Goldberg, Michael L. Blinov, Herbert M. Sauro, Ion I. Moraru, Jonathan R. Karr
To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators. org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool.
no code implementations • 5 Mar 2022 • Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks
Multiparty computation approaches to secure neural network inference traditionally rely on garbled circuits for securely executing nonlinear activation functions.
no code implementations • 30 Oct 2021 • Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, James Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim Hazelwood
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware.
1 code implementation • 29 Sep 2021 • Max Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks
Multiparty computation approaches to private neural network inference require significant communication between server and client, incur tremendous runtime penalties, and cost massive storage overheads.
1 code implementation • 10 Jun 2021 • Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher
We show that aggregated model updates in federated learning may be insecure.
1 code implementation • 18 May 2021 • Udit Gupta, Samuel Hsia, Jeff Zhang, Mark Wilkening, Javin Pombra, Hsien-Hsin S. Lee, Gu-Yeon Wei, Carole-Jean Wu, David Brooks
Thus, we design RecPipeAccel (RPAccel), a custom accelerator that jointly optimizes quality, tail-latency, and system throughput.
no code implementations • 5 Feb 2021 • Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj, Paul Whatmough, Aleksandra Faust, Sabrina Neuman, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi
Balancing a computing system for a UAV requires considering both the cyber (e. g., sensor rate, compute performance) and physical (e. g., payload weight) characteristics that affect overall performance.
no code implementations • 29 Jan 2021 • Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, Gu-Yeon Wei
Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment.
no code implementations • 27 Jan 2021 • Claire Poppett, Patrick Jelinsky, Julien Guy, Jerry Edelstein, Sharon Jelinsky, Jessica Aguilar, Ray Sharples, Jurgen Schmoll, David Bramall, Luke Tyas, Paul Martini, Kevin Fanning, Michael Levi, David Brooks, Peter Doel, Duan Yutong, Gregory Tarle, Erique Gaztanaga, Francisco Prada, the DESI Collaboration
The recently commissioned Dark Energy Spectroscopic Instrument (DESI) will measure the expansion historyof the universe using the Baryon Acoustic Oscillation technique.
Instrumentation and Methods for Astrophysics
no code implementations • 28 Nov 2020 • Thierry Tambe, Coleman Hooper, Lillian Pentecost, Tianyu Jia, En-Yu Yang, Marco Donato, Victor Sanh, Paul N. Whatmough, Alexander M. Rush, David Brooks, Gu-Yeon Wei
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks.
no code implementations • 10 Oct 2020 • Samuel Hsia, Udit Gupta, Mark Wilkening, Carole-Jean Wu, Gu-Yeon Wei, David Brooks
Deep learning based recommendation systems form the backbone of most personalized cloud services.
no code implementations • 27 Aug 2020 • Yu-Ching Chen, Xin Liu, Wei-Ting Liao, A. Miguel Holgado, Hengxiao Guo, Robert A. Gruendl, Eric Morganson, Yue Shen, Kaiwen Zhang, Tim M. C. Abbott, Michel Aguena, Sahar Allam, Santiago Avila, Emmanuel Bertin, Sunayana Bhargava, David Brooks, David L. Burke, Aurelio Carnero Rosell, Daniela Carollo, Matias Carrasco Kind, Jorge Carretero, Matteo Costanzi, Luiz N. da Costa, Tamara M. Davis, Juan De Vicente, Shantanu Desai, H. Thomas Diehl, Peter Doel, Spencer Everett, Brenna Flaugher, Douglas Friedel, Joshua Frieman, Juan García-Bellido, Enrique Gaztanaga, Karl Glazebrook, Daniel Gruen, Gaston Gutierrez, Samuel R. Hinton, Devon L. Hollowood, David J. James, Alex G. Kim, Kyler Kuehn, Nikolay Kuropatkin, Geraint F. Lewis, Christopher Lidman, Marcos Lima, Marcio A. G. Maia, Marisa March, Jennifer L. Marshall, Felipe Menanteau, Ramon Miquel, Antonella Palmese, Francisco Paz-Chinchón, Andrés A. Plazas, Eusebio Sanchez, Michael Schubnell, Santiago Serrano, Ignacio Sevilla-Noarbe, Mathew Smith, Eric Suchyta, Molly E. C. Swanson, Gregory Tarle, Brad E. Tucker, Tamas Norbert Varga, Alistair R. Walker
We present a systematic search for periodic light curves in 625 spectroscopically confirmed quasars with a median redshift of 1. 8 in a 4. 6 deg$^2$ overlapping region of the Dark Energy Survey Supernova (DES-SN) fields and the Sloan Digital Sky Survey Stripe 82 (SDSS-S82).
High Energy Astrophysical Phenomena Astrophysics of Galaxies
no code implementations • 25 Feb 2020 • DES Collaboration, Tim Abbott, Michel Aguena, Alex Alarcon, Sahar Allam, Steve Allen, James Annis, Santiago Avila, David Bacon, Alberto Bermeo, Gary Bernstein, Emmanuel Bertin, Sunayana Bhargava, Sebastian Bocquet, David Brooks, Dillon Brout, Elizabeth Buckley-Geer, David Burke, Aurelio Carnero Rosell, Matias Carrasco Kind, Jorge Carretero, Francisco Javier Castander, Ross Cawthon, Chihway Chang, Xinyi Chen, Ami Choi, Matteo Costanzi, Martin Crocce, Luiz da Costa, Tamara Davis, Juan De Vicente, Joseph DeRose, Shantanu Desai, H. Thomas Diehl, Jörg Dietrich, Scott Dodelson, Peter Doel, Alex Drlica-Wagner, Kathleen Eckert, Tim Eifler, Jack Elvin-Poole, Juan Estrada, Spencer Everett, August Evrard, Arya Farahi, Ismael Ferrero, Brenna Flaugher, Pablo Fosalba, Josh Frieman, Juan Garcia-Bellido, Marco Gatti, Enrique Gaztanaga, David Gerdes, Tommaso Giannantonio, Paul Giles, Sebastian Grandis, Daniel Gruen, Robert Gruendl, Julia Gschwend, Gaston Gutierrez, Will Hartley, Samuel Hinton, Devon L. Hollowood, Klaus Honscheid, Ben Hoyle, Dragan Huterer, David James, Mike Jarvis, Tesla Jeltema, Margaret Johnson, Stephen Kent, Elisabeth Krause, Richard Kron, Kyler Kuehn, Nikolay Kuropatkin, Ofer Lahav, Ting Li, Christopher Lidman, Marcos Lima, Huan Lin, Niall MacCrann, Marcio Maia, Adam Mantz, Jennifer Marshall, Paul Martini, Julian Mayers, Peter Melchior, Juan Mena, Felipe Menanteau, Ramon Miquel, Joe Mohr, Robert Nichol, Brian Nord, Ricardo Ogando, Antonella Palmese, Francisco Paz-Chinchon, Andrés Plazas Malagón, Judit Prat, Markus Michael Rau, Kathy Romer, Aaron Roodman, Philip Rooney, Eduardo Rozo, Eli Rykoff, Masao Sako, Simon Samuroff, Carles Sanchez, Alexandro Saro, Vic Scarpine, Michael Schubnell, Daniel Scolnic, Santiago Serrano, Ignacio Sevilla, Erin Sheldon, J. Allyn Smith, Eric Suchyta, Molly Swanson, Gregory Tarle, Daniel Thomas, Chun-Hao To, Michael A. Troxel, Douglas Tucker, Tamas Norbert Varga, Anja von der Linden, Alistair Walker, Risa Wechsler, Jochen Weller, Reese Wilkinson, Hao-Yi Wu, Brian Yanny, Zhuowen Zhang, Joe Zuntz
We perform a joint analysis of the counts and weak lensing signal of redMaPPer clusters selected from the Dark Energy Survey (DES) Year 1 dataset.
Cosmology and Nongalactic Astrophysics
2 code implementations • 13 Jan 2020 • Paul Whatmough, Marco Donato, Glenn Ko, Sae-Kyu Lee, David Brooks, Gu-Yeon Wei
The current trend for domain-specific architectures (DSAs) has led to renewed interest in research test chips to demonstrate new specialized hardware.
Hardware Architecture
no code implementations • 8 Jan 2020 • Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu
Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure.
Distributed, Parallel, and Cluster Computing
no code implementations • 10 Dec 2019 • Sam Likun Xi, Yuan YAO, Kshitij Bhardwaj, Paul Whatmough, Gu-Yeon Wei, David Brooks
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks.
no code implementations • 30 Nov 2019 • Siming Ma, David Brooks, Gu-Yeon Wei
We propose a new algorithm for training neural networks with binary activations and multi-level weights, which enables efficient processing-in-memory circuits with embedded nonvolatile memories (eNVM).
2 code implementations • 2 Oct 2019 • Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim Hazelwood, Andrew Hock, Xinyuan Huang, Atsushi Ike, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Tsuguchika Tabaru, Carole-Jean Wu, Lingjie Xu, Masafumi Yamazaki, Cliff Young, Matei Zaharia
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML.
no code implementations • 29 Sep 2019 • Thierry Tambe, En-Yu Yang, Zishen Wan, Yuntian Deng, Vijay Janapa Reddi, Alexander Rush, David Brooks, Gu-Yeon Wei
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequately capture the wide data distributions commonly seen in sequence transduction models.
no code implementations • 23 Aug 2019 • Udit Gupta, Brandon Reagen, Lillian Pentecost, Marco Donato, Thierry Tambe, Alexander M. Rush, Gu-Yeon Wei, David Brooks
The architecture is enhanced by a series of dynamic activation optimizations that enable compact storage, ensure no energy is wasted computing null operations, and maintain high MAC utilization for highly parallel accelerator designs.
1 code implementation • 13 Aug 2019 • Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim Hazelwood, David Brooks
State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers.
1 code implementation • 24 Jul 2019 • Yu Emma Wang, Gu-Yeon Wei, David Brooks
Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance.
7 code implementations • 6 Jun 2019 • Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang
The widespread application of deep learning has changed the landscape of computation in the data center.
2 code implementations • 8 Jan 2019 • Joseph DeRose, Risa H. Wechsler, Matthew R. Becker, Michael T. Busha, Eli S. Rykoff, Niall MacCrann, Brandon Erickson, August E. Evrard, Andrey Kravtsov, Daniel Gruen, Sahar Allam, Santiago Avila, Sarah Bridle, David Brooks, Elizabeth Buckley-Geer, Aurelio Carnero Rosell, Matias Carrasco Kind, Jorge Carretero, Francisco J. Castander, Ross Cawthon, Martin Crocce, Luiz N. da Costa, Christopher Davis, Juan De Vicente, Jörg P. Dietrich, Peter Doel, Alex Drlica-Wagner, Pablo Fosalba, Josh Frieman, Juan Garcia-Bellido, Gaston Gutierrez, Will G. Hartley, Devon L. Hollowood, Ben Hoyle, David J. James, Elisabeth Krause, Kyler Kuehn, Nikolay Kuropatkin, Marcos Lima, Marcio A. G. Maia, Felipe Menanteau, Christopher J. Miller, Ramon Miquel, Ricardo L. C. Ogando, Andrés Plazas Malagón, A. Kathy Romer, Eusebio Sanchez, Rafe Schindler, Santiago Serrano, Ignacio Sevilla-Noarbe, Mathew Smith, Eric Suchyta, Molly E. C. Swanson, Gregory Tarle, Vinu Vikram
We show that the weak-lensing shear catalog, redMaGiC galaxy catalogs and redMaPPer cluster catalogs provide plausible realizations of the same catalogs in the DES Y1 data by comparing their magnitude, color and redshift distributions, angular clustering, and mass-observable relations, making them useful for testing analyses that use these samples.
Cosmology and Nongalactic Astrophysics
no code implementations • 15 Feb 2018 • Yuhao Zhu, Gu-Yeon Wei, David Brooks
This paper takes the position that, while cognitive computing today relies heavily on the cloud, we will soon see a paradigm shift where cognitive computing primarily happens on network edges.
2 code implementations • 13 Nov 2017 • Brandon Reagen, Udit Gupta, Robert Adolf, Michael M. Mitzenmacher, Alexander M. Rush, Gu-Yeon Wei, David Brooks
This results in up to a 1. 51x improvement over the state-of-the-art.
1 code implementation • 23 Aug 2016 • Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, David Brooks
Fathom has been released online, and this paper focuses on understanding the fundamental performance characteristics of each model.