Welcome! You are invited to join a webinar: tinyML Talks webcast: 1) Qeexo's Runtime-Free Architecture for Efficient Deployment 2) Democratization of Artificial Intelligence (AI) to Small Scale Farmers. After registering, you will receive a confirmation email about joining the webinar.

"Qeexo’s Runtime-Free Architecture for Efficient Deployment of Neural Networks on Embedded Targets" Rajen Bhatt Director of Engineering Machine Learning, Qeexo Co Neural networks, including convolutional, feed-forward, recurrent, and convolutional-recurrent, are increasingly popular due to their recent successes in AI applications. Developing neural network models for tinyML applications can be very cumbersome due to constraints of embedded targets having low-power MCUs. Qeexo has developed a runtime-free architecture for efficiently converting TensorFlow-and-PyTorch-generated models to target libraries. This approach builds models which are orders of magnitude smaller than TensorFlow Lite Micro and does not compromise on latency or inference performance. "Democratization of Artificial Intelligence (AI) to Small Scale Farmers - a framework to deploy AI Models to Tiny IoT Edges that operate in constrained environments" Chandrasekar Vuppalapati Senior Vice President - Products & Programs Hanumayamma Innovations and Technologies Inc. Big Data surrounds us. Every minute, our smartphone collects huge amounts of data from geolocations to the next clickable item on an ecommerce site. Data has become one of the most important commodities for individuals and companies. Nevertheless, this data revolution has not touched every economic sector, especially rural economies, e.g., small farmers have been largely passed over the data revolution, in the developing countries due to infrastructure and compute constrained environments. Not only isthis a huge missed opportunity for big data companies, it is one of the significant obstacles in the path towards sustainable food and a huge inhibitor closing economic disparities. The purpose of the talk is to present the TinyML framework to deploy artificial intelligence models in constrained compute environments that enable remote rural areas and small farmers to join the data revolution.