Vijay Janapa Reddi

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Harvard University

Data Engineering for Everyone

Data engineering is one of the fastest-growing fields within machine lea...
02/23/2021 ∙ by Vijay Janapa Reddi, et al. ∙ 58 ∙ share

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

Deep reinforcement learning (RL) has made groundbreaking advancements in...
02/08/2021 ∙ by James Gleeson, et al. ∙ 0 ∙ share

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Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots

Building domain-specific architectures for autonomous aerial robots is c...
02/05/2021 ∙ by Srivatsan Krishnan, et al. ∙ 2 ∙ share

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MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It

MLPerf Mobile is the first industry-standard open-source mobile benchmar...
12/03/2020 ∙ by Vijay Janapa Reddi, et al. ∙ 8 ∙ share

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MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers

Executing machine learning workloads locally on resource constrained mic...
10/21/2020 ∙ by Colby Banbury, et al. ∙ 0 ∙ share

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TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems

Deep learning inference on embedded devices is a burgeoning field with m...
10/17/2020 ∙ by Robert David, et al. ∙ 0 ∙ share

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AI Tax: The Hidden Cost of AI Data Center Applications

Artificial intelligence and machine learning are experiencing widespread...
07/21/2020 ∙ by Daniel Richins, et al. ∙ 0 ∙ share

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Exceeding Conservative Limits: A Consolidated Analysis on Modern Hardware Margins

Modern large-scale computing systems (data centers, supercomputers, clou...
06/01/2020 ∙ by George Papadimitriou, et al. ∙ 0 ∙ share

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Benchmarking TinyML Systems: Challenges and Direction

Recent advancements in ultra-low-power machine learning (TinyML) hardwar...
03/10/2020 ∙ by Colby R. Banbury, et al. ∙ 0 ∙ share

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Quantized Neural Network Inference with Precision Batching

We present PrecisionBatching, a quantized inference algorithm for speedi...
02/26/2020 ∙ by Maximilian Lam, et al. ∙ 0 ∙ share

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MLPerf Inference Benchmark

Machine-learning (ML) hardware and software system demand is burgeoning....
11/06/2019 ∙ by Vijay Janapa Reddi, et al. ∙ 27 ∙ share

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MLPerf Training Benchmark

Machine learning is experiencing an explosion of software and hardware s...
10/02/2019 ∙ by Peter Mattson, et al. ∙ 6 ∙ share

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Quantized Reinforcement Learning (QUARL)

Recent work has shown that quantization can help reduce the memory, comp...
10/02/2019 ∙ by Srivatsan Krishnan, et al. ∙ 0 ∙ share

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GLADAS: Gesture Learning for Advanced Driver Assistance Systems

Human-computer interaction (HCI) is crucial for the safety of lives as a...
10/02/2019 ∙ by Ethan Shaotran, et al. ∙ 0 ∙ share

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

Conventional hardware-friendly quantization methods, such as fixed-point...
09/29/2019 ∙ by Thierry Tambe, et al. ∙ 0 ∙ share

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

Fully autonomous navigation using nano drones has numerous application i...
09/25/2019 ∙ by Bardienus P. Duisterhof, et al. ∙ 5 ∙ share

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Accelerator-level Parallelism

Future applications demand more performance, but technology advances hav...
07/02/2019 ∙ by Mark D. Hill, et al. ∙ 0 ∙ share

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One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers

Today's cloud service architectures follow a "one size fits all" deploym...
06/26/2019 ∙ by Matthew Halpern, et al. ∙ 4 ∙ share

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The Role of Compute in Autonomous Aerial Vehicles

Autonomous-mobile cyber-physical machines are part of our future. Specif...
06/24/2019 ∙ by Behzad Boroujerdian, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning for Cyber Security

The scale of Internet-connected systems has increased considerably, and ...
06/13/2019 ∙ by Thanh Thi Nguyen, et al. ∙ 0 ∙ share

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

We introduce Air Learning, an AI research platform for benchmarking algo...
06/02/2019 ∙ by Srivatsan Krishnan, et al. ∙ 0 ∙ share

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MAVBench: Micro Aerial Vehicle Benchmarking

Unmanned Aerial Vehicles (UAVs) are getting closer to becoming ubiquitou...
05/15/2019 ∙ by Behzad Boroujerdian, et al. ∙ 0 ∙ share

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Domain Specific Approximation for Object Detection

There is growing interest in object detection in advanced driver assista...
10/04/2018 ∙ by Ting-Wu Chin, et al. ∙ 0 ∙ share

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Vijay Janapa Reddi

Featured Co-authors

Jian Li

Fei Sun

Matei Zaharia

Gennady Pekhimenko

Matthew Mattina

Peter Bailis

Aleksandra Faust

Hanlin Tang

Mostafa El-Khamy

Gu-Yeon Wei

David Brooks

Data Engineering for Everyone

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

Machine Learning-Based Automated Design Space Exploration for Autonomous Aerial Robots

MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It

MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers

TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems

AI Tax: The Hidden Cost of AI Data Center Applications

Exceeding Conservative Limits: A Consolidated Analysis on Modern Hardware Margins

Benchmarking TinyML Systems: Challenges and Direction

Quantized Neural Network Inference with Precision Batching

MLPerf Inference Benchmark

MLPerf Training Benchmark

Quantized Reinforcement Learning (QUARL)

GLADAS: Gesture Learning for Advanced Driver Assistance Systems

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

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

Accelerator-level Parallelism

One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers

The Role of Compute in Autonomous Aerial Vehicles

Deep Reinforcement Learning for Cyber Security

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

MAVBench: Micro Aerial Vehicle Benchmarking

Domain Specific Approximation for Object Detection