Search Results for author: Vijay Janapa Reddi

Found 30 papers, 15 papers with code

Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles

no code implementations6 May 2022 Yu-Shun Hsiao, Siva Kumar Sastry Hari, Michał Filipiuk, Timothy Tsai, Michael B. Sullivan, Vijay Janapa Reddi, Vasu Singh, Stephen W. Keckler

The processing requirement of autonomous vehicles (AVs) for high-accuracy perception in complex scenarios can exceed the resources offered by the in-vehicle computer, degrading safety and comfort.

Autonomous Vehicles Frame

Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference

no code implementations5 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.

CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs

no code implementations5 Jan 2022 Shvetank Prakash, Tim Callahan, Joseph Bushagour, Colby Banbury, Alan V. Green, Pete Warden, Tim Ansell, Vijay Janapa Reddi

We present CFU Playground, a full-stack open-source framework that enables rapid and iterative design of machine learning (ML) accelerators for embedded ML systems.

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

no code implementations17 Nov 2021 Daniel Galvez, Greg Diamos, Juan Ciro, Juan Felipe Cerón, Keith Achorn, Anjali Gopi, David Kanter, Maximilian Lam, Mark Mazumder, Vijay Janapa Reddi

The People's Speech is a free-to-download 30, 000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset).

Speech Recognition

Tabula: Efficiently Computing Nonlinear Activation Functions for Private Neural Network Inference

1 code implementation29 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.

Few-Shot Keyword Spotting in Any Language

1 code implementation3 Apr 2021 Mark Mazumder, Colby Banbury, Josh Meyer, Pete Warden, Vijay Janapa Reddi

With just five training examples, we fine-tune the embedding model for keyword spotting and achieve an average F1 score of 0. 75 on keyword classification for 180 new keywords unseen by the embedding model in these nine languages.

Keyword Spotting Transfer Learning

Data Engineering for Everyone

no code implementations23 Feb 2021 Vijay Janapa Reddi, Greg Diamos, Pete Warden, Peter Mattson, David Kanter

This article shows that open-source data sets are the rocket fuel for research and innovation at even some of the largest AI organizations.

RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads

1 code implementation8 Feb 2021 James Gleeson, Srivatsan Krishnan, Moshe Gabel, Vijay Janapa Reddi, Eyal de Lara, Gennady Pekhimenko

Deep reinforcement learning (RL) has made groundbreaking advancements in robotics, data center management and other applications.

reinforcement-learning

AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs

no code implementations5 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.

TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems

1 code implementation17 Oct 2020 Robert David, Jared Duke, Advait Jain, Vijay Janapa Reddi, Nat Jeffries, Jian Li, Nick Kreeger, Ian Nappier, Meghna Natraj, Shlomi Regev, Rocky Rhodes, Tiezhen Wang, Pete Warden

We introduce TensorFlow Lite Micro (TF Micro), an open-source ML inference framework for running deep-learning models on embedded systems.

Benchmarking TinyML Systems: Challenges and Direction

1 code implementation10 Mar 2020 Colby R. Banbury, Vijay Janapa Reddi, Max Lam, William Fu, Amin Fazel, Jeremy Holleman, Xinyuan Huang, Robert Hurtado, David Kanter, Anton Lokhmotov, David Patterson, Danilo Pau, Jae-sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, Poonam Yadav

In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads.

Quantized Neural Network Inference with Precision Batching

no code implementations26 Feb 2020 Maximilian Lam, Zachary Yedidia, Colby Banbury, Vijay Janapa Reddi

We present PrecisionBatching, a quantized inference algorithm for speeding up neural network execution on traditional hardware platforms at low bitwidths without the need for retraining or recalibration.

Language Modelling Natural Language Inference +1

GLADAS: Gesture Learning for Advanced Driver Assistance Systems

no code implementations2 Oct 2019 Ethan Shaotran, Jonathan J. Cruz, Vijay Janapa Reddi

To the best of our knowledge, GLADAS is the first system of its kind designed to provide an infrastructure for further research into human-AV interaction.

Hand Gesture Recognition Hand-Gesture Recognition +1

QuaRL: Quantization for Sustainable Reinforcement Learning

1 code implementation2 Oct 2019 Srivatsan Krishnan, Maximilian Lam, Sharad Chitlangia, Zishen Wan, Gabriel Barth-Maron, Aleksandra Faust, Vijay Janapa Reddi

Motivated by the effectiveness of standard quantization techniques on reinforcement learning policies, we introduce a novel quantization algorithm, \textit{ActorQ}, for quantized actor-learner distributed reinforcement learning training.

Decision Making Quantization +1

AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference

no code implementations29 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.

Quantization

Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller

1 code implementation25 Sep 2019 Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury, William Fu, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi

We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter.

Autonomous Navigation Efficient Exploration +2

Deep Reinforcement Learning for Cyber Security

no code implementations13 Jun 2019 Thanh Thi Nguyen, Vijay Janapa Reddi

The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever.

Intrusion Detection reinforcement-learning

Air Learning: An AI Research Platform for Algorithm-Hardware Benchmarking of Autonomous Aerial Robots

1 code implementation2 Jun 2019 Srivatsan Krishnan, Behzad Boroujerdian, William Fu, Aleksandra Faust, Vijay Janapa Reddi

We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 40% longer trajectories in one of the environments.

Domain Specific Approximation for Object Detection

no code implementations4 Oct 2018 Ting-Wu Chin, Chia-Lin Yu, Matthew Halpern, Hasan Genc, Shiao-Li Tsao, Vijay Janapa Reddi

There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles.

Object Detection

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