Stochastic Neural Network Solution Wins the Xilinx Open Hardware Design Competition 2019 [Picture: Xilinx]
Ponnanna Kelettira Muthappa, a student of the international Master's program "Information Technology" (InfoTech) at the University of Stuttgart, has won the Xilinx Open Hardware Competition in the "Student" category. His winning entry, which was recognized at an awards ceremony at the Xilinx European headquarters in Dublin on 5 September 2019, contributes to ongoing efforts to make approaches based on neural networks (NNs) feasible for systems with severe resource restrictions. He designed an NN using the paradigm known as stochastic computing. "Stochastic computing offers extremely low-cost realization of arithmetic operations such as addition and multiplication that are heavily used in NNs. It also offers low power consumption and excellent fault-tolerance," explains Kelettira Muthappa, "for this reason, I was able to implement a fully-fledged convolutional neural network consisting of 11 layers and 236 neurons on a low-cost Xilinx development board." A wide range of artificial intelligence approaches that revolutionize today's technology are based on NNs. Their example applications are object detection (e.g., interpreting the road signs spotted during autonomous driving) and letter recognition (e.g., interpreting hand-written texts). So far, NNs suitable for complex tasks have been restricted to very large and powerful computing systems.
TO READ THIS ARTICLE, CREATE YOUR ACCOUNT
And extend your reading, free of charge and with no commitment.
