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306 results
2023
Matthew Adiletta, Jesmin Tithi, Emmanouil-Ioannis Farsarakis, Gerasimos Gerogiannis, Robert Adolf, Robert Benke, Sidharth Kashyap, Samuel Hsia, Kartik Lakhotia, Fabrizio Petrini, Gu-Yeon Wei, and David Brooks. 2023. “Characterizing the Scalability of Graph Convolutional Networks on Intel® PIUMA”. In IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). Raleigh, North Carolina
Matthew Adiletta, Jesmin Tithi, Emmanouil-Ioannis Farsarakis, Gerasimos Gerogiannis, Robert Adolf, Robert Benke, Sidharth Kashyap, Samuel Hsia, Kartik Lakhotia, Fabrizio Petrini, Gu-Yeon Wei, and David Brooks. 2023. “Characterizing the Scalability of Graph Convolutional Networks on Intel® PIUMA”. In IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS). Raleigh, North Carolina
Large-scale Graph Convolutional Network (GCN) inference on traditional CPU/GPU systems is challenging due to a large memory footprint, sparse computational patterns, and irregular memory accesses with poor locality. Intel's Programmable Integrated...
Samuel Hsia, Udit Gupta, Bilge Acun, Newsha Ardalani, Pan Zhong, Gu-Yeon Wei, David Brooks, and Carole-Jean Wu. 2023. “MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation”. In ASPLOS. Vancouver, Canada
Samuel Hsia, Udit Gupta, Bilge Acun, Newsha Ardalani, Pan Zhong, Gu-Yeon Wei, David Brooks, and Carole-Jean Wu. 2023. “MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation”. In ASPLOS. Vancouver, Canada
Deep learning recommendation systems serve personalized content under diverse tail-latency targets and input-query loads. In order to do so, state-of-the-art recommendation models rely on terabyte-scale embedding tables to learn user preferences over...
2022
Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, and David Brooks. 2022. “Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference”
Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, and David Brooks. 2022. “Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference”
Multiparty computation approaches to private neural network inference require significant communication between server and client, incur tremendous runtime penalties, and cost massive storage overheads. The primary source of these expenses is garbled...
L. Pentecost, A. Hankin, M. Donato, M. Hempstead, G.-Y. Wei, and D. Brooks. 2022. “NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories”. In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). Seoul, South Korea
L. Pentecost, A. Hankin, M. Donato, M. Hempstead, G.-Y. Wei, and D. Brooks. 2022. “NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded Non-Volatile Memories”. In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). Seoul, South Korea
Repeated off-chip memory accesses to DRAM drive up operating power for data-intensive applications, and SRAM technology scaling and leakage power limits the efficiency of embedded memories. Future on-chip storage will need higher density and energy...
Yu-Shun Hsiao, Siva Kumar Sastry Hari, Michał Filipiuk, Timothy Tsai, Michael B. Sullivan, Vijay Janapa Reddi, Vasu Singh, and Stephen W. Keckler. 2022. “Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles”. In ACM/IEEE/Design/Automation/Conference/(DAC). San Francisco, CA, USA
Yu-Shun Hsiao, Siva Kumar Sastry Hari, Michał Filipiuk, Timothy Tsai, Michael B. Sullivan, Vijay Janapa Reddi, Vasu Singh, and Stephen W. Keckler. 2022. “Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles”. In ACM/IEEE/Design/Automation/Conference/(DAC). San Francisco, CA, USA
Zishen Wan, Aqeel Anwar, Abdulrahman Mahmoud, Tianyu Jia, Yu-Shun Hsiao, Vijay Janapa Reddi, and Arijit Raychowdhury. 2022. “Frl-Fi: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems”. 2022 Design Automation and Test in Europe Conference (DATE)
Zishen Wan, Aqeel Anwar, Abdulrahman Mahmoud, Tianyu Jia, Yu-Shun Hsiao, Vijay Janapa Reddi, and Arijit Raychowdhury. 2022. “Frl-Fi: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems”. 2022 Design Automation and Test in Europe Conference (DATE)
Swarm intelligence is being increasingly deployed in autonomous systems, such as drones and unmanned vehicles. Federated reinforcement learning (FRL), a key swarm intelligence paradigm where agents interact with their own environments and cooperatively...
Tianyu Jia, En-Yu Yang, Yu-Shun Hsiao, Jonathan Cruz, David Brooks, Gu-Yeon Wei, and Vijay Janapa Reddi. 2022. “OMU: A Probabilistic 3D Occupancy Mapping Accelerator for Real-Time OctoMap at the Edge”. In DATE: Design, Automation, and Test in Europe (DATE)
Tianyu Jia, En-Yu Yang, Yu-Shun Hsiao, Jonathan Cruz, David Brooks, Gu-Yeon Wei, and Vijay Janapa Reddi. 2022. “OMU: A Probabilistic 3D Occupancy Mapping Accelerator for Real-Time OctoMap at the Edge”. In DATE: Design, Automation, and Test in Europe (DATE)
Thierry Tambe, David Brooks, and Gu-Yeon Wei. 2022. “Learnings from a HLS-Based High-Productivity Digital VLSI Flow”. In Workshop on Languages, Tools, and Techniques for Accelerator Design (LATTE’22)
Thierry Tambe, David Brooks, and Gu-Yeon Wei. 2022. “Learnings from a HLS-Based High-Productivity Digital VLSI Flow”. In Workshop on Languages, Tools, and Techniques for Accelerator Design (LATTE’22)
Thetwilight of Dennardscalinghasactivatedaglobaltrendtowards application-based hardware specialization. This trend is currently accelerating due to the surging democratization and deployment of machine learning on mobile and IoT compute platforms. At the...
Bo-Yuan Huang, Steven Lyubomirsky, Yi Li, Mike He, Thierry Tambe, Gus Henry Smith, Akash Gaonkar, Vishal Canumalla, Gu-Yeon Wei, Aarti Gupta, Zachary Tatlock, and Sharad Malik. 2022. “Specialized Accelerators and Compiler Flows: Replacing Accelerator APIs With a Formal Software Hardware Interface”
Bo-Yuan Huang, Steven Lyubomirsky, Yi Li, Mike He, Thierry Tambe, Gus Henry Smith, Akash Gaonkar, Vishal Canumalla, Gu-Yeon Wei, Aarti Gupta, Zachary Tatlock, and Sharad Malik. 2022. “Specialized Accelerators and Compiler Flows: Replacing Accelerator APIs With a Formal Software Hardware Interface”
Specialized accelerators are increasingly used to meet the power-performance goals of emerging applications such as machine learning, image processing, and graph analysis. Existing accelerator programming methodologies using APIs have several limitations...
Abdulrahman Mahmoud, Thierry Tambe, Tarek Aloui, David Brooks, and Gu-Yeon Wei. 2022. “GoldenEye: A Platform for Evaluating Emerging Numerical Data Formats in DNN Accelerators”
Abdulrahman Mahmoud, Thierry Tambe, Tarek Aloui, David Brooks, and Gu-Yeon Wei. 2022. “GoldenEye: A Platform for Evaluating Emerging Numerical Data Formats in DNN Accelerators”
This paper presents GoldenEye, a functional simulator with fault injection capabilities for common and emerging numerical formats, implemented for the PyTorch deep learning framework. Gold- enEye provides a unified framework for numerical format...
Matthew Adiletta, David Brooks, and Gu-Yeon Wei. 2022. Architectural Implications of Embedding Dimension During GCN on CPU and GPU. Cambridge: Harvard University
Matthew Adiletta, David Brooks, and Gu-Yeon Wei. 2022. Architectural Implications of Embedding Dimension During GCN on CPU and GPU. Cambridge: Harvard University
Graph Neural Networks (GNNs) are a class of neural networks designed to extract information from the graphical structure of data. Graph Convolutional Networks (GCNs) are a widely used type of GNN for transductive graph learning problems which apply...
2021
Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, and Gu-Yeon Wei. 2021. “RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference”. ASPLOS 2021: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Pp. 717–729
Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, and Gu-Yeon Wei. 2021. “RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference”. ASPLOS 2021: Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Pp. 717–729
Neural personalized recommendationmodelsareusedacrossawide Samuel Hsia Harvard University Cambridge, Massachusetts, USA shsia@g.harvard.edu David Brooks Harvard University Cambridge, Massachusetts, USA dbrooks@eecs.harvard.edu USA. ACM, New York, NY, USA...