Real-Time embedded machine learning for tensorial tactile data processing

Machine learning (ML) has increasingly been recently employed to provide solutions for difficult tasks, such as image and speech recognition, and tactile data processing achieving a near human decision accuracy. However, the efficient hardware implementation of ML algorithms in particular for real time applications is still a challenge. This paper presents the hardware architectures and implementation of a real time ML method based on tensorial kernel approach dealing with multidimensional input tensors. Two different hardware architectures are proposed and assessed. Results demonstrate the feasibility of the proposed implementations for real time classification. The proposed parallel architecture achieves a peak performance of 302 G-ops while consuming 1.14 W for the Virtex-7 XC7VX980T FPGA device overcoming state of the art solutions.