Mapping deep learning algorithms on systems-on chip

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This project aims to define an innovative approach for mapping deep learning neural networks of embedded autonomous systems communicating in the Internet of Things (IoT). Autonomous systems must be able to perform a series of operations determined by the outcome of the previous operation, or by reference to external circumstances monitored and measured by the system itself. Today's autonomous systems work well within defined and controlled situations, and there is huge potential for further development in this area. There are, however, still concerns and constraints. This is mainly because in the future IoT, autonomous systems will exchange a continuous flow of massive data, and each system must be able to handle the data and activate appropriate responses quickly, safely, and securely, while consuming little power. This requires a variety of complex decision-making, learning and data processing by these systems. This proposed research project will develop solutions to key challenges in the design, implementation, and deployment of autonomous systems in the IoT, namely: - New methods of adapting deep learning, the key algorithms in the autonomous systems of the future, to the tight performance and power constraints specific to edge computing and autonomous systems; - New tools for automatic, optimized, deployment of these extremely complex learning algorithms on embedded platforms. We are particularly concerned with a set of communicating algorithms, specific to learning, computer vision and voice recognition, mapped on dedicated platforms integrating multi-core architectures and neural network accelerators, such as the Synopsys EV6x.