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306 results
2021
Udit Gupta, Samuel Hsia, Jeff Zhang, Mark Wilkening, Javin Pombra, Hsien-Hsin S. Lee, Gu-Yeon Wei, Carole-Jean Wu, and David Brooks. 2021. “RecPipe: Co-Designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance”. MICRO ’21: MICRO-54: 54th Annual IEEE ACM International Symposium on Microarchitecture, Pp. 870–884
Udit Gupta, Samuel Hsia, Jeff Zhang, Mark Wilkening, Javin Pombra, Hsien-Hsin S. Lee, Gu-Yeon Wei, Carole-Jean Wu, and David Brooks. 2021. “RecPipe: Co-Designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance”. MICRO ’21: MICRO-54: 54th Annual IEEE ACM International Symposium on Microarchitecture, Pp. 870–884
Deep learning recommendation systems must provide high quality, personalized content under strict tail-latency targets and high system loads. This paper presents RecPipe, a system to jointly optimize recommendation quality and inference performance...
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, and Michael Mitzenmacher. 2021. “Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix”
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, and Michael Mitzenmacher. 2021. “Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix”
We show that aggregated model updates in federated learning may be insecure. An untrusted central server may disaggregate user updates from sums of updates across participants given repeated observations, enabling the server to recover privileged...
M. M. Sharifi, L. Pentecost, R. Rajaei, A. Kazemi, Q. Lou, G.-Y. Wei, D. Brooks, K. Ni, X. S. Hu, M. Niemier, and M. Donato. 2021. “Application-Driven Design Exploration for Dense Ferroelectric Embedded Non-Volatile Memories”
M. M. Sharifi, L. Pentecost, R. Rajaei, A. Kazemi, Q. Lou, G.-Y. Wei, D. Brooks, K. Ni, X. S. Hu, M. Niemier, and M. Donato. 2021. “Application-Driven Design Exploration for Dense Ferroelectric Embedded Non-Volatile Memories”
The memory wall bottleneck is a key challenge across many data-intensive applications. Multi-level FeFET-based embedded non-volatile memories are a promising solution for denser and more energy-efficient on-chip memory. However, reliable multi-level cell...
Zishen Wan, Aqeel Anwar, Yu-Shun Hsiao, Tianyu Jia, Vijay Janapa Reddi, and Arijit Raychowdhury. 2021. “Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems”. In 58th ACM IEEE Design Automation Conference (DAC)
Zishen Wan, Aqeel Anwar, Yu-Shun Hsiao, Tianyu Jia, Vijay Janapa Reddi, and Arijit Raychowdhury. 2021. “Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems”. In 58th ACM IEEE Design Automation Conference (DAC)
Learning-based navigation systems are widely used in autonomous applications, such as robotics, unmanned vehicles and drones. Specialized hardware accelerators have been proposed for high-performance and energy-efficiency for such navigational tasks...
Yu-Shun Hsiao, Zishen Wan, Tianyu Jia, Radhika Ghosal, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, and Vijay Janapa Reddi. 2021. “Mavfi: An End-to-End Fault Analysis Framework With Anomaly Detection and Recovery for Micro Aerial Vehicles”
Yu-Shun Hsiao, Zishen Wan, Tianyu Jia, Radhika Ghosal, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, and Vijay Janapa Reddi. 2021. “Mavfi: An End-to-End Fault Analysis Framework With Anomaly Detection and Recovery for Micro Aerial Vehicles”
Reliability and safety are critical in autonomous machine services, such as autonomous vehicles and aerial drones. In this paper, we first present an open-source Micro Aerial Vehicles (MAVs) reliability analysis framework, MAVFI, to characterize transient...
Thierry Tambe, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks, and Gu-Yeon Wei. 2021. “A 25mm2 SoC for IoT Devices With 18ms Noise Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET”. International Solid-State Circuits Conference (ISSCC’21)
Thierry Tambe, En-Yu Yang, Glenn G. Ko, Yuji Chai, Coleman Hooper, Marco Donato, Paul N. Whatmough, Alexander M. Rush, David Brooks, and Gu-Yeon Wei. 2021. “A 25mm2 SoC for IoT Devices With 18ms Noise Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET”. International Solid-State Circuits Conference (ISSCC’21)
Automatic speech recognition (ASR) using deep learning is essential for user interfaces on IoT devices. However, previously published ASR chips [4-7] do not consider realistic operating conditions, which are typically noisy and may include more than one...
Thierry Tambe, Coleman Hooper, Lillian Pentecost, Tianyu Jia, En-Yu Yang, Marco Donato, Victor Sanh, Paul Whatmough, Alexander M. Rush, David Brooks, and Gu-Yeon Wei. 2021. “EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference”. IEEE/ACM/International/Symposium/on/Microarchitecture/(MICRO/2021)
Thierry Tambe, Coleman Hooper, Lillian Pentecost, Tianyu Jia, En-Yu Yang, Marco Donato, Victor Sanh, Paul Whatmough, Alexander M. Rush, David Brooks, and Gu-Yeon Wei. 2021. “EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference”. IEEE/ACM/International/Symposium/on/Microarchitecture/(MICRO/2021)
Transformer-based language models such as BERT provide significant accuracy improvement for a multitude of natural language processing (NLP) tasks. However, their hefty computational and memory demands make them challenging to deploy to resource...
Bo-Yuan Huang, Steven Lyubomirsky, Thierry Tambe, Yi Li, Mike He, Gus Smith, Gu-Yeon Wei, Aarti Gupta, Sharad Malik, and Zachary Tatlock. 2021. “From DSLs to Accelerator-Rich Platform Implementations: Addressing the Mapping Gap”. Workshop on Languages, Tools, and Techniques for Accelerator Design (LATTE’21)
Bo-Yuan Huang, Steven Lyubomirsky, Thierry Tambe, Yi Li, Mike He, Gus Smith, Gu-Yeon Wei, Aarti Gupta, Sharad Malik, and Zachary Tatlock. 2021. “From DSLs to Accelerator-Rich Platform Implementations: Addressing the Mapping Gap”. Workshop on Languages, Tools, and Techniques for Accelerator Design (LATTE’21)
Sabrina M. Neuman, Brian Plancher, Thomas Bourgeat, Thierry Tambe, Srinivas Devadas, and Vijay Janapa Reddi. 2021. “Robomorphic Computing: A Design Methodology for Domain-Specific Accelerators Parameterized by Robot Morphology”. Architectural Support for Programming Languages and Operating Systems (ASPLOS’21), Pp. 674–686
Sabrina M. Neuman, Brian Plancher, Thomas Bourgeat, Thierry Tambe, Srinivas Devadas, and Vijay Janapa Reddi. 2021. “Robomorphic Computing: A Design Methodology for Domain-Specific Accelerators Parameterized by Robot Morphology”. Architectural Support for Programming Languages and Operating Systems (ASPLOS’21), Pp. 674–686
Robotics applications have hard time constraints and heavy computational burdens that can greatly benefit from domain-specific hardware accelerators. For the latency-critical problem of robot motion planning and control, there exists a performance gap of...
2020
Samuel Hsia, Udit Gupta, Wilkening Mark, Carole Wu, Gu-Yeon Wei, and David Brooks. 2020. “Cross-Stack Workload Characterization of Deep Recommendation Systems”. In 2020 IEEE International Symposium on Workload Characterization (IISWC)
Samuel Hsia, Udit Gupta, Wilkening Mark, Carole Wu, Gu-Yeon Wei, and David Brooks. 2020. “Cross-Stack Workload Characterization of Deep Recommendation Systems”. In 2020 IEEE International Symposium on Workload Characterization (IISWC)
Deep learning based recommendation systems form the backbone of most personalized cloud services. Though the computer architecture community has recently started to take notice of deep recommendation inference, the resulting solutions have taken wildly...
Glenn Ko, Yuji Chai, Marco Donato, Paul Whatmough, Tambe Thierry, Rob Rutenbar, Gu Wei, and Gu Wei. 2020. “A Scalable Bayesian Inference Accelerator for Unsupervised Learning”. In IEEE Hot Chips 31 Symposium. Palo Alto, CA, USA
Glenn Ko, Yuji Chai, Marco Donato, Paul Whatmough, Tambe Thierry, Rob Rutenbar, Gu Wei, and Gu Wei. 2020. “A Scalable Bayesian Inference Accelerator for Unsupervised Learning”. In IEEE Hot Chips 31 Symposium. Palo Alto, CA, USA
This article consists only of a collection of slides from the author's conference presentation.
Thierry Tambe, En-Yang, Zishen Wan, Yuntian Deng, Vijay Reddi, Alexander Rush, David Brooks, and Gu-Yeon Wei. 2020. “Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference”. In . San Francisco, CA, USA: Design Automation Conference (DAC 2020)
Thierry Tambe, En-Yang, Zishen Wan, Yuntian Deng, Vijay Reddi, Alexander Rush, David Brooks, and Gu-Yeon Wei. 2020. “Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference”. In . San Francisco, CA, USA: Design Automation Conference (DAC 2020)
Conventional hardware-friendly quantization methods, such asfixed-point or integer, tend to perform poorly at very low preci-sion as their shrunken dynamic ranges cannot adequately capturethe wide data distributions commonly seen in sequence transduc-tion...