200 citations · 493 across the 20 of their papers we have counts for
21 papers
AppSelectBench: Application-Level Tool Selection Benchmark
Tianyi Chen, Michael Solodko, Sen Wang +14
Computer Using Agents (CUAs) are increasingly equipped with external tools, enabling them to perform complex and realistic tasks. For CUAs to operate effectively, application selec…
WINA: Weight Informed Neuron Activation for Accelerating Large Language Model Inference
Sihan Chen, Dan Zhao, Jongwoo Ko +5
The growing computational demands of large language models (LLMs) make efficient inference and activation strategies increasingly critical. While recent approaches, such as Mixture…
WinClick: GUI Grounding with Multimodal Large Language Models
Zheng Hui, Yinheng Li, Dan zhao +3
Graphical User Interface (GUI) tasks are vital for automating workflows such as software testing, user interface navigation. For users, the GUI is the most intuitive platform for i…
Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression
Xiaoyi Qu, David Aponte, Colby Banbury +5
Structured pruning and quantization are fundamental techniques used to reduce the size of deep neural networks (DNNs) and typically are applied independently. Applying these techni…
Fast Data Aware Neural Architecture Search via Supernet Accelerated Evaluation
Emil Njor, Colby Banbury, Xenofon Fafoutis
Tiny machine learning (TinyML) promises to revolutionize fields such as healthcare, environmental monitoring, and industrial maintenance by running machine learning models on low-p…
HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning
Tianyi Chen, Xiaoyi Qu, David Aponte +7
Structured pruning is one of the most popular approaches to effectively compress the heavy deep neural networks (DNNs) into compact sub-networks while retaining performance. The ex…
Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
Colby Banbury, Emil Njor, Andrea Mattia Garavagno +7
Tiny machine learning (TinyML) co-locates models with sensors on microcontrollers, where small models (which are disproportionately sensitive to label noise) and bespoke binary tas…
MobileNetV4 -- Universal Models for the Mobile Ecosystem
Danfeng Qin, Chas Leichner, Manolis Delakis +11
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the…
Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers
Shvetank Prakash, Matthew Stewart, Colby Banbury +4
The sustained growth of carbon emissions and global waste elicits significant sustainability concerns for our environment's future. The growing Internet of Things (IoT) has the pot…
Edge Impulse: An MLOps Platform for Tiny Machine Learning
Shawn Hymel, Colby Banbury, Daniel Situnayake +13
Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware tar…
XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse
Hyoukjun Kwon, Krishnakumar Nair, Jamin Seo +15
Real-time multi-task multi-model (MTMM) workloads, a new form of deep learning inference workloads, are emerging for applications areas like extended reality (XR) to support metave…
DataPerf: Benchmarks for Data-Centric AI Development
Mark Mazumder, Colby Banbury, Xiaozhe Yao +42
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithf…
Machine Learning Sensors
Pete Warden, Matthew Stewart, Brian Plancher +6
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from c…
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots
Sabrina M. Neuman, Brian Plancher, Bardienus P. Duisterhof +8
Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stress-tests the challenges of ML system design is tiny robot learning, the…
CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
Shvetank Prakash, Tim Callahan, Joseph Bushagour +5
Need for the efficient processing of neural networks has given rise to the development of hardware accelerators. The increased adoption of specialized hardware has highlighted the…
Widening Access to Applied Machine Learning with TinyML
Vijay Janapa Reddi, Brian Plancher, Susan Kennedy +21
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloe…
MLPerf Tiny Benchmark
Colby Banbury, Vijay Janapa Reddi, Peter Torelli +19
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the…
Few-Shot Keyword Spotting in Any Language
Mark Mazumder, Colby Banbury, Josh Meyer +2
We introduce a few-shot transfer learning method for keyword spotting in any language. Leveraging open speech corpora in nine languages, we automate the extraction of a large multi…
Benchmarking TinyML Systems: Challenges and Direction
Colby R. Banbury, Vijay Janapa Reddi, Max Lam +14
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by…
Quantized Neural Network Inference with Precision Batching
Maximilian Lam, Zachary Yedidia, Colby Banbury +1
We present PrecisionBatching, a quantized inference algorithm for speeding up neural network execution on traditional hardware platforms at low bitwidths without the need for retra…
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller
Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz +5
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable infere…