The automotive industry is rapidly adopting AI to improve driving safety, with advanced driver assistance systems (ADAS) and autonomous driving technologies becoming more widespread. As AI models become more sophisticated, vehicles increasingly rely on powerful system on chips (SoCs) and large volumes of high resolution sensor data, especially from cameras, to achieve accurate environmental perception.
However, this surge in data and processing requirements is straining current vehicle architectures. Key SoC resources such as memory bandwidth and GPU capacity are becoming bottlenecks, potentially limiting ADAS performance. To solve this, a new electronic control unit (ECU) architecture introduces a discrete image signal processor (ISP) that offloads image processing tasks from the main SoC, freeing critical compute resources for AI workloads.
indie will delve into this topic, covering the benefits of this discrete ISP approach and presenting a real-world ADAS/autonomous driving implementation.
