4 research outputs found
Datasheets for Machine Learning Sensors
Machine learning (ML) sensors offer a new paradigm for sensing that enables
intelligence at the edge while empowering end-users with greater control of
their data. As these ML sensors play a crucial role in the development of
intelligent devices, clear documentation of their specifications,
functionalities, and limitations is pivotal. This paper introduces a standard
datasheet template for ML sensors and discusses its essential components
including: the system's hardware, ML model and dataset attributes, end-to-end
performance metrics, and environmental impact. We provide an example datasheet
for our own ML sensor and discuss each section in detail. We highlight how
these datasheets can facilitate better understanding and utilization of sensor
data in ML applications, and we provide objective measures upon which system
performance can be evaluated and compared. Together, ML sensors and their
datasheets provide greater privacy, security, transparency, explainability,
auditability, and user-friendliness for ML-enabled embedded systems. We
conclude by emphasizing the need for standardization of datasheets across the
broader ML community to ensure the responsible and effective use of sensor
data
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
TinyML4D: Scaling Embedded Machine Learning Education in the Developing World
Embedded machine learning (ML) on low-power devices, also known as "TinyML," enables intelligent applications on accessible hardware and fosters collaboration across disciplines to solve real-world problems. Its interdisciplinary and practical nature makes embedded ML education appealing, but barriers remain that limit its accessibility, especially in developing countries. Challenges include limited open-source software, courseware, models, and datasets that can be used with globally accessible heterogeneous hardware. Our vision is that with concerted effort and partnerships between industry and academia, we can overcome such challenges and enable embedded ML education to empower developers and researchers worldwide to build locally relevant AI solutions on low-cost hardware, increasing diversity and sustainability in the field. Towards this aim, we document efforts made by the TinyML4D community to scale embedded ML education globally through open-source curricula and introductory workshops co-created by international educators. We conclude with calls to action to further develop modular and inclusive resources and transform embedded ML into a truly global gateway to embedded AI skills development