Search Results for author: Colby Banbury

Found 12 papers, 6 papers with code

Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers

no code implementations27 Jan 2023 Shvetank Prakash, Matthew Stewart, Colby Banbury, Mark Mazumder, Pete Warden, Brian Plancher, Vijay Janapa Reddi

This article discusses both the potential of these TinyML applications to address critical sustainability challenges, as well as the environmental footprint of this emerging technology.

Edge Impulse: An MLOps Platform for Tiny Machine Learning

no code implementations2 Nov 2022 Shawn Hymel, Colby Banbury, Daniel Situnayake, Alex Elium, Carl Ward, Mat Kelcey, Mathijs Baaijens, Mateusz Majchrzycki, Jenny Plunkett, David Tischler, Alessandro Grande, Louis Moreau, Dmitry Maslov, Artie Beavis, Jan Jongboom, Vijay Janapa Reddi

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 targets.

Machine Learning Sensors

1 code implementation7 Jun 2022 Pete Warden, Matthew Stewart, Brian Plancher, Colby Banbury, Shvetank Prakash, Emma Chen, Zain Asgar, Sachin Katti, Vijay Janapa Reddi

Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications.

BIG-bench Machine Learning

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

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

Few-Shot Keyword Spotting in Any Language

2 code implementations3 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

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

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