Publications

Our publications provide insight into some of our leading-edge research.

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2020

The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems
Ahmet Inci, Evgeny Bolotin, Yaosheng Fu, Gal Dalal, Shie Mannor, David Nellans, Diana Marculescu
GPU-Trident: Efficient Modeling of Error Propagation in GPU Programs
Abdul Rehman Anwer, Guanpeng Li, Karthik Pattabiraman, Michael B. Sullivan, Timothy Tsai, Siva Hari
CaSA: End-to-end Quantitative Security Analysis of Randomly Mapped Caches
Thomas Bourgeat, Jules Drean, Yuheng Yang, Lillian Tsai, Joel Emer, Mengjia Yan
Locality-Centric Data and Threadblock Management for Massive GPUs
Mahmoud Khairy, Vadim Nikiforov, David Nellans, Timothy G. Rogers
HarDNN: Fine-Grained Vulnerability Evaluation and Protection for Convolutional Neural Networks
Abdulrahman Mahmoud, Siva Hari, Christopher W. Fletcher, Sarita V. Adve, Charbel Sakr, Naresh Shanbhag, Pavlo Molchanov, Michael B. Sullivan, Timothy Tsai, Steve Keckler
How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel Emer
Planaria: Dynamic Architecture Fission for Spatial Multi-Tenant Acceleration of Deep Neural Networks
Soroush Ghodrati, Byung Hoon Ahn, Joon Kyung Kim, Sean Kinzer, Brahmendra Reddy Yatham, Navateja Alla, Hardik Sharma, Mohammad Alian, Eiman Ebrahimi, Nam Sung Kim, Cliff Young, Hadi Esmaeilzadeh
PyTorchFI: A Runtime Perturbation Tool for DNNs
Abdulrahman Mahmoud, Neeraj Aggarwal, Alex Nobbe, Jose Rodrigo Sanchez Vicarte, Sarita V. Adve, Christopher W. Fletcher, Iuri Frosio, Siva Hari
EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal In GPUs
Seung Won Min, Vikram Sharma Mailthody, Zaid Qureshi, Jinjun Xiong, Eiman Ebrahimi, Wen-mei Hwu
Making Convolutions Resilient via Algorithm-Based Error Detection Techniques
Siva Hari, Michael B. Sullivan, Timothy Tsai, Steve Keckler
There’s Plenty of Room at the Top: What Will Drive Computer Performance after Moore’s Law?
Charles E. Leiserson, Neil C. Thompson, Joel Emer, Bradley C. Kuszmaul, Butler W. Lampson, Daniel Sanchez , Tao B. Schardl
Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs
Esha Chouske, Michael B. Sullivan, Mike O'Connor, Mattan Erez, Jeff Pool, David Nellans, Steve Keckler
An In-Network Architecture for Accelerating Shared-Memory Multiprocessor Collectives
Benjamin Klenk, Ted Jiang, Greg Thorson, Larry Dennison
Estimating Silent Data Corruption Rates Using a Two-Level Model
Siva Hari, Paolo Rech, Timothy Tsai, Mark Stephenson, Arslan Zulfiqar, Michael B. Sullivan, Philip Shirvani, Paul Racunas, Joel Emer, Steve Keckler
MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings
Hyoukjun Kwon, Prasanth Chatarasi, Vivek Sarkar, Tushar Krishna, Michael Pellauer, Angshuman Parashar
BYOC: A "Bring Your Own Core" Framework for Heterogeneous-ISA Research
Jonathan Balkind, Katie Lim, Michael Schaffner, Fei Gao, Grigory Chirkov, Ang Li, Alexey Lavrov, Tri M. Nguyen, Yaosheng Fu, Florian Zaruba, Kunal Gulati, Luca Benini, David Wentzlaf
Safecracker: Leaking Secrets through Compressed Caches
Po-An Tsai, Andres Sanchez, Christopher W. Fletcher, Daniel Sanchez
IEEE Micro Top Picks in Computer Architecture
RealityCheck: Bringing Modularity, Hierarchy, and Abstraction to Automated Microarchitectural Memory Consistency Verification
Yatin A. Manerkar, Daniel Lustig, Margaret Martonosi
Feature Map Vulnerability Evaluation in CNNs
Abdulrahman Mahmoud, Siva Hari, Christopher W. Fletcher, Sarita V. Adve, Charbel Sakr, Naresh Shanbhag, Pavlo Molchanov, Michael B. Sullivan, Timothy Tsai, Steve Keckler
HarDNN: Feature Map Vulnerability Evaluation in CNNs
Abdulrahman Mahmoud, Siva Hari, Christopher W. Fletcher, Sarita V. Adve, Charbel Sakr, Naresh Shanbhag, Pavlo Molchanov, Michael B. Sullivan, Timothy Tsai, Steve Keckler
HMG: Extending Cache Coherence Protocols Across Modern Hierarchical Multi-GPU Systems
Xiaowei Ren, Daniel Lustig, Evgeny Bolotin, Aamer Jaleel, Oreste Villa, David Nellans
Speculative Reconvergence for Improved SIMT Efficiency
Sana Damani, Daniel Johnson, Mark Stephenson, Eddie Yan, Olivier Giroux, Michael McKeown, Steve Keckler

2017

Toward Standardized Near-Data Processing with Unrestricted Data Placement for GPUs
Gwangsun Kim, Niladrish Chatterjee, Mike O'Connor, Kevin Hsieh
Understanding Error Propagation in Deep Learning Neural Network (DNN) Accelerators and Applications
Guanpeng Li, Siva Hari, Michael B. Sullivan, Timothy Tsai, Karthik Pattabiraman, Joel Emer, Steve Keckler
Fine-Grained DRAM: Energy-Efficient DRAM for Extreme Bandwidth Systems
Mike O'Connor, Niladrish Chatterjee, Donghyuk Lee, John Wilson, Aditya Agrawal, Steve Keckler, William Dally
Xylem: Enhancing Vertical Thermal Conduction in 3D Processor-Memory Stacks
Aditya Agrawal, Josep Torrellas, Sachin Idgunji
Ambit: In-Memory Accelerator for Bulk Bitwise Operations Using Commodity DRAM Technology
Vivek Seshadri, Donghyuk Lee, Thomas Mullins, Hasan Hassan, Amirali Boroumand, Jeremie Kim, Michael A. Kozuch, Onur Mutlu, Phillip B. Gibbons, Todd C. Mowry
Detecting and Mitigating Data-Dependent DRAM Failures by Exploiting Current Memory Content
Samira Khan, Chris Wilkerson, Zhe Wang, Alaa R. Alameldeen, Donghyuk Lee, Onur Mutlu
RTLCheck: Verifying Memory Consistency in RTL Designs
Yatin A. Manerkar, Daniel Lustig, Margaret Martonosi, Michael Pellauer
IEEE Micro Top Picks in Computer Architecture (Honorable Mention)
Beyond the Socket: NUMA-Aware GPUs
Ugljesa Milic, Oreste Villa, Evgeny Bolotin, Akhil Arunkumar, Eiman Ebrahimi, Aamer Jaleel, Alex Ramirez, David Nellans
Weak Memory Models with Matching Axiomatic and Operational Definitions
Sizhuo Zhang, Muralidaran Vijayaraghavan, Daniel Lustig, Arvind
BATMAN: Maximizing Bandwidth Utilization of Hybrid Memory Systems
Chiachen Chou, Aamer Jaleel, Moinuddin Qureshi