Deploying complex perception pipelines—such as multi-sensor fusion or high-resolution object tracking—on low-power edge devices presents a significant computational challenge.
Our R&D focuses on hardware-aware algorithm design, utilizing techniques like int8 quantization, tensor scaling, and operator fusion tailored to specific neural processing units (NPUs) or embedded GPUs.
By optimizing the algorithm for the target hardware architecture, we enable real-time, low-latency inference even in severely SWaP-constrained environments.