Vector-symbolic architectures as an algebraic programming language for neuromorphic computers

Making the AI computations fast and more energy efficient is the current grand challenge of Computer Science and Electrical Engineering.

Solving it is essential for more rapid emergence of intelligent technologies such as self-driving cars, autonomous robots, large scale information retrieval systems. At the present time modern AI applications  expose   fundamental   limitations  of the current computers. To solve this challenge novel computing hardware such as neuromorphic processors is emerging.

The neuromorphic (NM) computers consume only a fraction of the   energy compared to the current technology and for certain tasks can be 1,000 times more energy efficient.

The purpose of this project is to contribute to the solution of the great AI challenge by solving the challenges of the neuromorphic computing.

With this project I aim at bridging the NM computing usability gap by developing an algebraic programming methodology for intuitive realization of AI models on the neuromorphic hardware; and showcase it by developing novel AI algorithms tailored for efficient execution on NM hardware.