Traditional radar systems often rely on periodic sampling of a point cloud, where data is collected at regular intervals regardless of the presence or absence of relevant signals. This approach can lead to inefficiencies, especially in environments where significant events are sparse. Event-based computing, on the other hand, processes data only when meaningful events occur, such as the detection of a moving object. This leads to a more responsive system, capable of real-time processing with minimal latency.

Radar-Lidar systems often operate in power-constrained environments, such as on unmanned aerial vehicles (UAVs) or in remote sensing stations. Traditional radar processing can be resource-intensive, continuously consuming power even when no significant data is being processed. Event-based computing dramatically reduces power consumption by activating processing resources only when needed.

Radar systems generate vast amounts of data, much of which may be irrelevant or redundant. Handling such large data volumes can overwhelm processing systems and networks, leading to delays and inefficiencies. Event-based computing reduces data overload by focusing only on relevant events, which results in more manageable data streams and easier storage.

Event-based computing offers a high degree of scalability, making it suitable for a wide range of radar applications, from small-scale systems like automotive radars or lidars to large-scale systems used in weather monitoring and defense. Moreover, event-based systems are highly adaptable; they can be fine-tuned to different environments and conditions without requiring significant changes to the underlying hardware.