11 research outputs found
Edge Impulse: An MLOps Platform for Tiny Machine Learning
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. Current TinyML workflows are plagued by
fragmented software stacks and heterogeneous deployment hardware, making ML
model optimizations difficult and unportable. We present Edge Impulse, a
practical MLOps platform for developing TinyML systems at scale. Edge Impulse
addresses these challenges and streamlines the TinyML design cycle by
supporting various software and hardware optimizations to create an extensible
and portable software stack for a multitude of embedded systems. As of Oct.
2022, Edge Impulse hosts 118,185 projects from 50,953 developers
Widening Access to Applied Machine Learning with TinyML
Broadening access to both computational and educational resources is critical
to diffusing machine-learning (ML) innovation. However, today, most ML
resources and experts are siloed in a few countries and organizations. In this
paper, we describe our pedagogical approach to increasing access to applied ML
through a massive open online course (MOOC) on Tiny Machine Learning (TinyML).
We suggest that TinyML, ML on resource-constrained embedded devices, is an
attractive means to widen access because TinyML both leverages low-cost and
globally accessible hardware, and encourages the development of complete,
self-contained applications, from data collection to deployment. To this end, a
collaboration between academia (Harvard University) and industry (Google)
produced a four-part MOOC that provides application-oriented instruction on how
to develop solutions using TinyML. The series is openly available on the edX
MOOC platform, has no prerequisites beyond basic programming, and is designed
for learners from a global variety of backgrounds. It introduces pupils to
real-world applications, ML algorithms, data-set engineering, and the ethical
considerations of these technologies via hands-on programming and deployment of
TinyML applications in both the cloud and their own microcontrollers. To
facilitate continued learning, community building, and collaboration beyond the
courses, we launched a standalone website, a forum, a chat, and an optional
course-project competition. We also released the course materials publicly,
hoping they will inspire the next generation of ML practitioners and educators
and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series:
https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin
Benchmarking TinyML Systems : Challenges and Direction
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 the lack of a widely accepted benchmark for these systems. Benchmarking allows us to measure and thereby systematically compare, evaluate, and improve the performance of systems and is therefore fundamental to a field reaching maturity. 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. Furthermore, we present our four benchmarks and discuss our selection methodology. Our viewpoints reflect the collective thoughts of the TinyMLPerf working group that is comprised of over 30 organizations
DataPerf: Benchmarks for Data-Centric AI Development
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 faithfulness of the underlying problems. Neglecting
the fundamental importance of data has given rise to inaccuracy, bias, and
fragility in real-world applications, and research is hindered by saturation
across existing dataset benchmarks. In response, we present DataPerf, a
community-led benchmark suite for evaluating ML datasets and data-centric
algorithms. We aim to foster innovation in data-centric AI through competition,
comparability, and reproducibility. We enable the ML community to iterate on
datasets, instead of just architectures, and we provide an open, online
platform with multiple rounds of challenges to support this iterative
development. The first iteration of DataPerf contains five benchmarks covering
a wide spectrum of data-centric techniques, tasks, and modalities in vision,
speech, acquisition, debugging, and diffusion prompting, and we support hosting
new contributed benchmarks from the community. The benchmarks, online
evaluation platform, and baseline implementations are open source, and the
MLCommons Association will maintain DataPerf to ensure long-term benefits to
academia and industry.Comment: NeurIPS 2023 Datasets and Benchmarks Trac
Widening Access to Applied Machine Learning With TinyML
Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies
CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs
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 need for more agile design flows for
hardware-software co-design and domain-specific optimizations. 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. Our
toolchain integrates open-source software, open-source RTL generators, and
open-source FPGA tools for synthesis, place, and route. This full-stack
framework gives the users access to explore bespoke architectures that are
customized and co-optimized for embedded ML. The rapid,
deploy-profile-optimization feedback loop lets ML hardware and software
developers achieve significant returns out of a relatively small investment in
customization. Using CFU Playground's design loop, we show substantial speedups
between 55 and 75. The soft CPU coupled with the accelerator
opens up a new, rich design space between the two components that we explore in
an automated fashion using Vizier, a black-box optimization service
Machine Learning Sensors
Machine learning sensors represent a paradigm shift for the future of
embedded machine learning applications. Current instantiations of embedded
machine learning (ML) suffer from complex integration, lack of modularity, and
privacy and security concerns from data movement. This article proposes a more
data-centric paradigm for embedding sensor intelligence on edge devices to
combat these challenges. Our vision for "sensor 2.0" entails segregating sensor
input data and ML processing from the wider system at the hardware level and
providing a thin interface that mimics traditional sensors in functionality.
This separation leads to a modular and easy-to-use ML sensor device. We discuss
challenges presented by the standard approach of building ML processing into
the software stack of the controlling microprocessor on an embedded system and
how the modularity of ML sensors alleviates these problems. ML sensors increase
privacy and accuracy while making it easier for system builders to integrate ML
into their products as a simple component. We provide examples of prospective
ML sensors and an illustrative datasheet as a demonstration and hope that this
will build a dialogue to progress us towards sensor 2.0