Announcing tinyML IN PERSON Meetup on December 16th, 2022

Date: Friday, December 16th
Time: 7am to 9 am PST
Location: Qualcomm Canada Inc
105 Commerce Valley Dr W · Markham, ON

Welcome back, tinyML Toronto community, to our first in-person event! On December 16th, we will get together for a morning of discussion, presentation and collaboration at Qualcomm AI’s office in Markham, Ontario. There will be 2 exciting and informative presentations from leading Toronto AI experts at Qualcomm and Deeplite. There will be light snacks and refreshments provided by our host and attendees are encouraged to network before/after the event. All guests are required to register beforehand. SPACE IS LIMITED SO RSVP ASAP!

“An overview of the Snapdragon 8 Gen 2 Sensing Hub”
Speaker: Andrew Kostic, Sr. Director of Product Management, Qualcomm Technologies, Inc.

At the heart of the Snapdragon 8 mobile platform is the Qualcomm AI Engine. With the Snapdragon 8 Gen 2, we are updating the generation Sensing Hub with a dedicated dual-core AI Accelerator, for low power and always-on AI processing.

The Snapdragon 8 Gen 2 Sensing Hub with AI Accelerators offers double the performance of the previous generation, allowing for:

• Improved always-on camera
• Better AI-noise suppression, for better calls wherever you are, including your hybrid work
• New low power AI use cases developers are still thinking of thanks to its improved programmability and additional memory
  In this talk, we’ll highlight some of the capabilities of the 4th gen Sensing Hub, shown at the recent Snapdragon Summit 2022 | Snapdragon Tech Event (qualcomm.com)

“Expanding possibilities for Edge AI with compressed & quantized CNN models”
Speaker: Ehsan Saboori, CTO and co-founder of Deeplite

The emergence of Deep Neural Networks (DNNs) on embedded and low-end devices holds tremendous potential to expand the adoption of AI technologies to wider audiences. However, making DNNs applicable for inference on such devices using techniques such as quantization and model compression, while maintaining model accuracy, remains a challenge for production. Ehsan will be presenting results from a novel inference engine (DeepliteRT) and compression framework (Deeplite Neutrino), developed by Deeplite Inc. He will discuss how Deeplite Neutrino automatically compresses and quantizes CNN models, and give real-world case studies of DeepliteRT, a first-of-its kind engine to run PyTorch deep learning models at real 2bit and 1bit precision.