Moe is currently a Lead Embedded Software Engineer in the New Product Innovation (NPI) Software Team at Dyson. He has been actively contributing to various stages of several new products across different categories at Dyson. He and his team are responsible for ensuring the next generation of connected products are utilising the right technologies that are refined in readiness to transition to the delivery teams. Core to this is exploring the proposition by developing proof of concept integrations and systems. Some key technologies include: IoT/Connected Systems; Embedded Linux systems; Bluetooth Low Energy Moe is also an Industrial Mentor for the Dyson Institute of Technology (DIET) and the University of Bristol (UoB).
In this talk, Moe will discuss the exciting possibilities of implementing machine learning algorithms on small, connected embedded devices, such as in smart homes, wearable devices, and industrial automation. He will highlight the opportunities, as well as the challenges that come with this, given the devices' limited processing power and memory capacity.
By the end of this talk, you will have a better understanding of the possibilities of implementing machine learning on connected embedded devices. You will have new insights about the strategies and considerations necessary to develop efficient algorithms and models, and optimize the performance on these devices.
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