Hardware and heterogeneous CNN for vision systems

With the increasing need for monitoring and control in daily life, artificial intelligence (AI) is gaining more autonomy to process massive data with minimal human intervention. Among AI algorithms, Convolutional Neural Networks (CNNs) are increasingly used as the core of many intelligent systems, including those that run on mobile and embedded devices. However, the CNN execution is computationally demanding especially on resource-limited. This makes the CNN use on embedded devices quite challenging. It is therefore natural to turn to hardware development to both speed up the CNN process, reduce the cost of resources and allow on-board processing. In this context, this paper proposes a full hardware implementation as well as an heterogeneous acceleration of the CNN process using Field Programmable Gate Arrays (FPGA). Both recognition accuracy and computational time were analysed for all the proposed implementation types that are namely: the software implementation (C and Python implementations), the full hardware implementation (VHDL), and the heterogeneous implementation (VHDL and C). From these results, we note that the hardware acceleration using FPGA is validated with a factor of 100 compared to the software implementation.