Mauro Lanza
Development of a high-performance Linux device driver for a custom SNN accelerator for Xilinx FPGA boards.
Rel. Stefano Di Carlo, Alessandro Savino, Alessio Carpegna. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2022
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Abstract
This thesis aims to create a driver for a custom Spiking Neural Network which is able to recognize numbers from images with an IDX format. The driver will manage the gather of image information and the interface connection with the SNN. To do that the Network will be placed into the FPGA of a Xilinx ZC702 board ©.The connection will take place on an Advanced Microntroller Bus Architecture (AMBA) accessible via an AXI4 peripheral, to do such a thing a Slave interface will be created and attached on a custom VHDL block that acts as a bridge between the ARM core processor of the board and the SNN.
The ultimate goal will be to boot Linux (in particular the Ubuntu 20.04LTS build) from an SD card inserted into the board, call the driver from a C application which takes the image in the IDX format and start the data exchange with the SNN on the FPGA
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