Cover story
Designing intelligent edge systems
Edge AI only becomes meaningful when it leaves the lab and operates reliably in the fi eld
By Felipe Leiva, Technical Project Manager, Aetina I
n real deployments, system architects quickly face constraints that go far beyond raw computing performance. Latency must be predictable, power budgets are limited, connectivity
is not always guaranteed, and systems are expected to run continuously for years in harsh environments. At Aetina, Edge AI is approached as a system-level challenge
rather than a standalone computing problem. By combining NVIDIA Jetson platforms with industrial-grade hardware design, optimised software stacks and deployment experience, Aetina delivers edge AI devices that are already operating in traffic infrastructure, smart agriculture, industrial inspection, and intelligent buildings. With the upcoming launch of NVIDIA Jetson Thor, this approach naturally extends toward more advanced and autonomous edge systems.
From NVIDIA Jetson modules to Aetina edge AI devices The NVIDIA Jetson platform provides the accelerated computing foundation for edge AI, but turning a Jetson module into a deployable product requires additional engineering. Power stability, thermal behaviour, enclosure design, I/O fl exibility and long-term availability all infl uence whether a system can survive outside controlled environments. Aetina’s DeviceEdge portfolio addresses these requirements
by offering complete systems built on NVIDIA Jetson Orin platforms and prepared for future Jetson Thor designs. These systems are validated for continuous operation, wide temperature ranges and integration into existing infrastructure, allowing customers to focus on AI application development rather than hardware constraints.
06 February 2026
www.electronicsworld.co.uk
Understanding performance at the edge; what TOPS really means When comparing edge AI platforms, performance is often expressed in TOPS, or Trillions of Operations Per Second. TOPS is a practical indicator of how many AI operations a processor can execute per second, and it directly affects how many models, camera streams, or AI tasks can run simultaneously. In real deployments, higher TOPS enables more complex
models, higher input resolution, multi-stream processing and lower latency. However, TOPS alone does not guarantee system performance. Efficient use of frameworks such as NVIDIA CUDA and TensorRT is essential to translate theoretical performance into real-world results.
High-performance traffi c monitoring with NVIDIA Jetson AGX Orin Traffic monitoring and enforcement remain one of the most demanding AI use cases. Systems must process multiple high-resolution video streams in real time, often under extreme weather conditions, while maintaining high accuracy and uptime. Aetina’s AIE-PX11, AIE-PX12, AIE-PX21 and AIE-PX22,
powered by NVIDIA Jetson AGX Orin, are widely deployed in intelligent traffic projects across Europe. Depending on confi guration, Jetson AGX Orin delivers up to 275TOPS, making it suitable for heavy vision workloads such as license plate recognition, vehicle classifi cation, speed monitoring and incident detection. These systems leverage NVIDIA CUDA for parallel
processing, TensorRT for low-latency inference and DeepStream
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