Widening Access to Applied Machine Learning with TinyML

  title={Widening Access to Applied Machine Learning with TinyML},
  author={Vijay Janapa Reddi and Brian Plancher and Susan Kennedy and Laurence Moroney and Pete Warden and Anant Agarwal and Colby R. Banbury and Massimo Banzi and Matthew Bennett and Benjamin Brown and Sharad Chitlangia and Radhika Ghosal and Sarah Grafman and Rupert Jaeger and Srivatsan Krishnan and Maximilian Lam and Daniel Leiker and Cara Mann and Mark Mazumder and Dominic Pajak and Dhilan Ramaprasad and J. Evan Smith and Matthew P. Stewart and Dustin Tingley},
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… 
Intelligence at the Extreme Edge: A Survey on Reformable TinyML
This work presents a survey on reformable TinyML solutions with the proposal of a novel taxonomy for ease of separation and explores the workflow of TinyML and analyzes the identified deployment schemes and the scarcely available benchmarking tools.
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
A framework using Semantic Web technologies to enable the joint management of TinyML models and IoT devices at scale, from modeling information to discovering possible combinations and benchmarking, and eventually facilitate TinyML component exchange and reuse is proposed.
Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices
A structure in which the part that preprocesses externally input data in the TinyML application is distributed to the hardware, and resistor–transistor logic, which perform not only windowing using the Hann function, but also acquire audio raw data is added to the inter-integrated circuit sound module that collects audio data inThe voice-recognition application.
Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case
Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution,


Utilizing crowdsourcing and machine learning in education: Literature review
It is found that using either crowdsourcing or machine learning in the online courses will enhance the interactions between the students and merging both the machine learning to the crowd wisdom will increase the accuracy and the efficiency of education.
Addressing the challenges of a new digital technologies curriculum: MOOCs as a scalable solution for teacher professional development
This paper develops a MOOC to deliver free computing content and pedagogy to teachers with the integration of social media to support knowledge exchange and resource building and describes the process of developing the initiative, participant engagement and experiences.
AI-Based Digital Assistants
It is foreseen that AI-based digital assistants will become a key element in the future of work, and are expected to take over routine tasks from humans and to free up time and resources for more demanding tasks.
Federated Learning: Strategies for Improving Communication Efficiency
Two ways to reduce the uplink communication costs are proposed: structured updates, where the user directly learns an update from a restricted space parametrized using a smaller number of variables, e.g. either low-rank or a random mask; and sketched updates, which learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling.
A TinyMLaaS Ecosystem for Machine Learning in IoT: Overview and Research Challenges
This paper describes how the "as-a-Service" model is bound to TinyML, by providing an overview of the concept and introducing the design requirements and building blocks that can make TinyMLaaS reality.
Arduino for Teaching Embedded Systems . Are Computer Scientists and Engineering Educators Missing the Boat ?
In this work, we look at the Arduino as a design platform for a course on embedded systems and ask the question, is the Arduino platform suitable for teaching computer engineers and computer
The Cambridge Handbook of Multimedia Learning
During the past 10 years, the field of multimedia learning has emerged as a coherent discipline with an accumulated research base that has never been synthesized and organized in a handbook. The
MOOCs for Teacher Professional Development : Reflections , and Suggested Actions
Teacher Professional Development (TPD) has become a major policy priority within education systems worldwide. But keeping teachers professionally up-to-date and providing them professional
Temporal Convolution for Real-time Keyword Spotting on Mobile Devices
A temporal convolution for real-time KWS on mobile devices that exploits temporal convolutions with a compact ResNet architecture and achieves more than \textbf{385x} speedup on Google Pixel 1 and surpass the accuracy compared to the state-of-the-art model.
Model Cards for Model Reporting
This work proposes model cards, a framework that can be used to document any trained machine learning model in the application fields of computer vision and natural language processing, and provides cards for two supervised models: One trained to detect smiling faces in images, and one training to detect toxic comments in text.