By leveraging affordable virtual resources, simulation can evaluate both essential functionalities and edge cases, significantly cutting costs and timelines of traditional validation methods. These results offer valuable insights to refine designs and justify investments in physical hardware for real-world use. This whitepaper explores the tuning and simulation of Image Signal Processors (ISPs) and emphasizes the need for developing simulation tools for modern mobility and transportation engineering.

ISPs within System-on-Chips (SoCs) process raw image and video data from sensors like RGB and infrared (IR). They play a key role in converting sensor outputs into usable image formats for applications ranging from basic photography to advanced machine vision systems. This whitepaper focuses on ISPs used with RGB sensors, which can be calibrated to work seamlessly with various camera sensors by adjusting sensor-specific parameters.

Simulating ISP pipelines allows developers to efficiently test and refine algorithms, paving the way for precise and rapid engineering support. ISP tuning involves optimizing algorithms to enhance image data from camera sensors. These tools help solution architects test and evaluate sensor choices, eliminating the need for expensive and time-consuming hardware integrations during the research and exploration phases.

MulticoreWare has collaborated with autonomous vehicle OEMs to configure image sensors optimized for machine vision applications, ensuring seamless processing of image data for training and deployment of machine learning (ML) models. A significant challenge arose when configuring the ISP on the hardware platform to perform minimal color processing, as required by the specific needs of the training data for ML models. This process demanded extensive manual fine-tuning of the ISP’s color processing module, which proved time-consuming and labor-intensive. 

Although the hardware platform vendor provided a GUI-based tuning tool, it lacked an offline full pipeline simulator for RAW image frames. To address this limitation, MulticoreWare developed a custom offline simulation workflow that significantly reduced tuning effort and improved efficiency, underscoring the value of simulation tools in modern engineering workflows.