What we're about

FIRST SEATTLE AREA MEETUP TO BE ANNOUNCED SOON!

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 Seattle, WA.

• 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 (1)

tinyML Talks by Shivy Yohanandan from Xailient

Network event

Online event

Announcing tinyML Talks on July 6th, 2021

IMPORTANT: Please register here
https://us02web.zoom.us/webinar/register/9016231071401/WN_5zPMgLhpS_620I_LWfOdbw

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)
Shivy Yohanandan, Co-founder and Chief Technology Officer, Xailient
"Cracking a 600 million year old secret to fit computer vision on the edge"

The ultimate goal of AI IoT is to be aware of our surroundings through sensors which can respond in real-time so we can be more selective with how we use and manage our limited resources, which reduces business and environmental cost. But the big problem with AI IoT is that current AI uses more energy to process IoT data than the energy it’s trying to save, which is a paradox. The main cause is expensive algorithm families like YOLO, SSD, R-CNN, and their derivatives, which account for most of the computer vision algorithms used by everyone!

YOLOs and SSDs do object detection (a staple in most computer vision) by shrinking the full resolution image to 416x416 or 300x300 and then doing both localization and classification on this shrunken image. But you’ve now lost over 95% of information from the original image, which is why accuracy, robustness and generalizability seems to be poor, especially when trying to scale across many IoT sensors (e.g. cameras). In addition to this inherent design flaw, these models are huge and computationally expensive, which is why everyone is trying to fit them on the edge by shrinking these models. However, this often results in losing even more accuracy on a model that was already inaccurate to begin with!

Xailient solved this problem by cracking a 600 million year old secret in biological vision: selective attention and salience. The secret mechanism shows us how to split object detection into two separate models: detection and classification. This results in Xailient’s detector being only 44 KB -- 5000x smaller than YOLO! You can then use your own flavor of classifier to process each detected ROI one-by-one, except now using a crop from the original image, thus preserving more information for better accuracy. So we’ve solved both model size and accuracy in one hit!

Dr. Shivy Yohanandan is the co-founder and Chief Technology Officer at Xailient – the computer vision platform that is revolutionizing Artificial Intelligence by teaching algorithms how to process images and video like humans! He holds a PhD in Artificial Intelligence and Computer Science but started his career as a Neuroscientist and Bioengineer from the University of Melbourne. Passionate about vision, Shivy spent 4 years bringing vision to the blind by helping build Australia’s first bionic eye as a Research Engineer. Previously, Shivy worked as a research scientist for 3 years at IBM Research in AI for healthcare including computer vision in medical imaging and building a brain-machine interface to decode brainwaves for controlling a robotic arm.

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

#@#

Photos (79)