What we're about

Based on the success of our tinyML meetup group in the Bay Area, we are branching out to other areas of the country, starting with Austin, TX.

• What is the purpose of the group?
To spread the word and educate the industry on "tinyML" (broadly defined as machine learning architectures, devices, techniques, tools and approaches capable of performing on-device analytics for a variety of sensing modalities--vision, audio, motion, environmental, human health monitoring etc.) at “mW” or below power range targeting predominately battery operated devices. The tinyML meetup group is an informal monthly gathering of researchers and practitioners working on various aspects of machine learning technologies (hardware-algorithms/networks- software-application) at the extreme low-power regime to share latest developments in this fast growing field and promote collaborations throughout the ecosystem. The format will be presentations with Q&A followed by networking.

• Who should join?
Experts in machine learning technologies at the edge, especially in the low power battery operated regime. This includes hardware architects, software engineers, systems engineers, ASIC designers, algorithms and application developers, low power sensor providers and end users. “Newbees”, i.e. people interested in joining this field and getting up to speed by listening start-of-the-art presentations and interacting with established players are very welcome to join too, both from the industry and the academia.

• What will you do at your events?
Communicate to the attendees the “latest and greatest” in tinyML by watching a presentation from a tinyML expert from the industry or the academia and interfacing with the member of the tinyML Community.

Upcoming events (4+)

tinyML Talks by Vijay Janapa Reddi from Harvard University

Network event

Needs a location

Announcing tinyML Talks on May 24th, 2022

IMPORTANT: Please register here
https://us02web.zoom.us/webinar/register/4716521138045/WN_Ipaqfjz7QvCo6dQ5IzS07Q

Once registered, you will receive a link and dial in information to teleconference by email, that you can also add to your calendar.

8:00 AM - 9:00 AM Pacific Daylight Time (PDT)
Vijay Janapa Reddi, Associate Professor, Harvard University
"MLOps for TinyML: Challenges & Directions in Operationalizing TinyML at Scale"

Over eighty percent or more of companies that attempt to integrate machine learning into operational applications fail. How could this be? Many organizations underestimate the difficulty of implementing ML. This talk emphasizes the significance of machine learning operations (MLOps) in scaling TinyML to enterprise-scale deployments that provide real-world value. Training and deploying a machine learning model on a single tiny embedded device is one thing; it is quite another to scale to thousands of devices. TinyML adds a number of embedded ecosystem-specific impediments to the conventional machine learning deployment pipeline, hence considerably complicating ML deployment even further. To address these myriad issues, the talk introduces a seven-stage MLOps architecture for operationalizing TinyML successfully. These stages range from ML model development for a fleet of heterogeneous devices to continuous monitoring for detecting data drift and everything in-between. The framework is a comprehensive end-to-end workflow for scaling TinyML deployments from a proof of concept to a real-world solution.

Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML organization that aims to accelerate ML innovation. He also serves on the MLCommons board of directors.

We encourage you to register earlier since on-line broadcast capacity may be limited.

Note: tinyML Talks slides and videos will be available on the tinyML website and tinyML YouTube Channel afterwards, for those who missed the live session. Please take a moment and subscribe to the YouTube channel today: https://www.youtube.com/tinyML?sub_confirmation=1

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tinyML Talks by George Gekov from Arm

Network event

Needs a location

Announcing tinyML UK in-person event

IMPORTANT: Please register here
https://www.meetup.com/tinyML-Enabling-low-Power-ML-at-the-edge-Cambridge-England/events/286006961/

Date: 25th May 2022
Time: 6 pm BST / 10 am PDT
Location: 2 Quayside, Cambridge CB5 8AB

George Gekov, Application Engineer, Arm
"How to accelerate ML inference in silicon, while maintaining low-power consumption"

George Gekov is a technologist at heart. He is part of Arm’s Machine Learning Team, working on the software for next generation Neural Processing Units. Previously, George was creating example IoT applications on low-power microcontrollers.

Note: tinyML Talks slides and videos will be available on the tinyML website and tinyML YouTube Channel afterwards, for those who missed the live session. Please take a moment and subscribe to the YouTube channel today: https://www.youtube.com/tinyML?sub_confirmation=1

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tinyML Talks by Fadi Alsaleem from University of Nebraska - Lincoln

Network event

Needs a location

Announcing tinyML Talks on May 31st, 2022

IMPORTANT: Please register here
https://us02web.zoom.us/webinar/register/3616506448592/WN_4fepOiukShqApBqkxxw_gg

Once registered, you will receive a link and dial in information to teleconference by email, that you can also add to your calendar.

8:00 AM - 9:00 AM Pacific Daylight Time (PDT)
Assistant Professor, Durham School of Architectural Engineering and Construction, Mechanical Engineering Department (Courtesy appointment), University of Nebraska - Lincoln
"ML using micro-electromechanical system (MEMS)"

This talk covers a new technology that enables a sensor such as a wearable accelerometer to provide high-level processed information such as step counts or type of activity rather than the simple raw acceleration measurement. This new technology is based on a micro-electromechanical system (MEMS) where a network of them is designed to locally perform advanced algorithms. The algorithms will be coded in the mechanical responses of the sensing elements of these multiple-coupled MEMS devices that simultaneously capture the measurement of interest such as acceleration. As a result, the MEMS network will perform computing at the sensing physical layer and will require very little power, eliminating the need for a microprocessor and eliminating the need for the energy-hungry circuitry for conditioning and reading the output of the traditional sensor. The new sensing/computing technology has been demonstrated in multiple applications such as human activity recognition, simple signal classification, and mobile robot.

Dr. Alsaleem joined the college of engineering at the University of Nebraska at Lincoln (UNL) in August 2016. Before this assignment, he worked for multiple years in the industry including four years as a Senior Lead Algorithm Engineer at Emerson Electric Inc to develop novel (cloud-based) sensor monitoring and learning algorithms used for fault diagnostics for mechanical systems. His current and future potential research goals are to vertically advance the fields of intelligent wearable sensing technologies and artificial intelligence algorithms and their use in many health and medical applications. In this research area, he has more than 10 awarded patents, more than 100 publications, presentations, and invited talks, and over 6 million total (near 1.5 million to his research team) of active funding to support his research work.

We encourage you to register earlier since on-line broadcast capacity may be limited.

Note: tinyML Talks slides and videos will be available on the tinyML website and tinyML YouTube Channel afterwards, for those who missed the live session. Please take a moment and subscribe to the YouTube channel today: https://www.youtube.com/tinyML?sub_confirmation=1

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tinyML Talks by Radha Agarwal from Indian Institute of Science

Network event

Needs a location

Announcing tinyML Talks on June 6th, 2022

IMPORTANT: Please register here
https://us02web.zoom.us/webinar/register/2616528069052/WN_cuxapF_3RAq36VVwDr7c5Q

Once registered, you will receive a link and dial in information to teleconference by email, that you can also add to your calendar.

8:00 AM - 9:00 AM Pacific Daylight Time (PDT)
Radha Agarwal, Master’s student, Indian Institute of Science, Bangalore
"tinyRadar: mmWave Radar-based Human Activity Classification for Edge Computing"

Most of the current systems for patient monitoring, elderly, and child care are camera-based and often require cloud computing. But, camera-based systems pose a privacy risk, and cloud computing can lead to higher latency, data theft, and connectivity issues. Why face these challenges in the current era of intelligent sensing modalities with tiny and edge solutions?

This talk will give insights about a tinyML-based single-chip radar solution for on-edge sensing and detection of the environment. Thus, the hassle can be avoided by using the tiny radar, which protects privacy, and works in all weather and lighting conditions while sensing with a contactless interface. At the same time, edge computing on it gives a small form factor that makes it robust enough for remote deployments. This end-to-end pipeline from sensing to detection is demonstrated for real-time human activity classification using a Texas Instruments IWR6843 millimeter-wave radar board. The edge implementation of the 8-bit quantized inference engine is done on the radar’s Cortex-R4F MCU using the CMSIS-NN custom APIs.

Radha Agarwal is a master’s student in the Department of Electronic Systems Engineering, Indian Institute of Science (IISc), Bangalore, India. She is a Texas Instruments scholar working on a mmWave radar project in the NeuRonICS Lab, IISc under the guidance of Dr. Chetan Singh Thakur. She has done several projects involving machine learning, embedded systems, and digital design using FPGA. Her fields of interest include applications of machine learning and embedded systems. In 2020, she was awarded the prestigious Governor’s silver medal for her academic performance in B.Tech.

We encourage you to register earlier since on-line broadcast capacity may be limited.

Note: tinyML Talks slides and videos will be available on the tinyML website and tinyML YouTube Channel afterwards, for those who missed the live session. Please take a moment and subscribe to the YouTube channel today: https://www.youtube.com/tinyML?sub_confirmation=1

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Past events (133)

tinyML Talks by Manivannan Sivan from Valeo

Needs a location

Photos (197)