Ever wondered how the world was before the internet was invented? You might soon wonder how the world would survive without self-driving cars and Advanced Driver Assistance Systems (ADAS). The extensive research taking place in this rapidly evolving field is making self-driving cars futuristic and more reliable. The goal of this research is to design and develop hardware Convolutional Neural Network (CNN) accelerators for self-driving cars, that can process audio and visual sensory information. The idea is to imitate a human brain that takes audio and visual data as input while driving. To achieve a single die that can process both audio and visual sensory information, it takes two different kinds of accelerators where one processes visual data from images captured by a camera and the other processes audio information from audio recordings. The Convolutional Neural Network AI algorithm is chosen to classify images and audio data.
Image CNN (ICNN) is used to classify images and Audio CNN (ACNN) to classify any sound of significance while driving. The two networks are designed from scratch and implemented in software and hardware. The software implementation of the two AI networks utilizes the Numpy library of Python, while the hardware implementation is done in Verilog®. CNN is trained to classify between three classes of objects, Cars, Lights, and Lanes, while the other CNN is trained to classify sirens from an emergency vehicle, vehicle horn, and speech.
Electrical Engineering (MS)
Department, Program, or Center
Electrical Engineering (KGCOE)
Mark A. Indovina
Gillela, Rohini Jaychandre, "Design of Hardware CNN Accelerators for Audio and Image Classification" (2020). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus