SensiML to Present Technical Session at Silicon Labs’ Works With Conference

SensiML Corporation, a leading developer of AI tools for building intelligent Internet of Things endpoints, announced that SensiML’s Chief Technology Officer, Chris Knorowski, will be presenting a technical session at the Silicon Labs annual Works With conference. The event will take place virtually from August 22-24, 2023.

Title:                                How to Ensure Success Implementing TinyML Solutions

                                           Tuesday Aug 22, 8:00 AM – 8:50 AM, Pacific Time
Date/Time:                Tuesday Aug 22, 9:00 PM – 9:50 PM, Pacific Time

What you will learn:   The session aims to provide developers with practical guidance and invaluable insights into the challenges and common pitfalls often encountered when developing sensor-based machine learning  inference models. Knorowski’s session will delve into the best practices and strategies to overcome the challenges faced during the development and implementation of AI/ML solutions on resource-constrained devices.

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“SensiML is thrilled to join Silicon Labs’ Works With Conference once again this year,” said Chris Knorowski, CTO of SensiML. “As pioneers in the TinyML industry, we are dedicated to helping developers successfully navigate the intricacies of implementing sensor-based machine learning solutions on microcontrollers.”

SensiML is a valued partner for Silicon Labs and has been participating in the Works With Conference since 2020. To learn more and to register for the conference, SensiML, a subsidiary of QuickLogic offers cutting-edge software that enables ultra-low power IoT endpoints that implement AI to transform raw sensor data into meaningful insight at the device itself. The company’s flagship solution, the SensiML Analytics Toolkit, provides an end-to-end development platform spanning data collection, labeling, algorithm and firmware auto-generation, and testing. The SensiML Toolkit supports a broad array of microcontroller cores, including Silicon Labs’ EFR32 and EFM32 lines as well as optimizations for Silicon Labs’ MVP AI accelerator core.

SOURCE : PRNewswire