Aetina Corporation has introduced its Mini Series Edge AI systems, featuring models such as the AIE-CO23/33-S1, AIE-CN33/43-A1, AIB-MO23/33-S1, and AIB-MN33/43-S1.
These systems are powered by NVIDIA Jetson Orin Nano with Super Mode and NVIDIA Jetson Orin NX modules, aiming to deliver robust vision AI and generative AI inference in compact industrial settings. Designed for real-world applications, they address the challenges of space limitation, energy efficiency, reliability, and system integration flexibility.
As AI solutions transition from proof-of-concept phases to operational deployment, compact edge AI systems are essential for processing data locally, connecting with cameras and sensors, and maintaining performance in demanding conditions.
Aetina’s Mini Series satisfies these needs by providing powerful AI computing capabilities, support for MIPI and PoE cameras, industrial-grade reliability, and long-term product availability. The series boasts a compact form factor, with models like the palmsize, fanless AIE-CO23/33-S1 and AIE-CN33/43-A1 offering a 132 x 91.5 x 68.5 mm design, supporting dual PoE cameras, and delivering up to 100 TOPS of performance. For tighter spaces, the AIB-MO23/33-S1 and AIB-MN33/43-S1 models feature a slimmer 127 x 85.75 x 28.45 mm form factor and deliver up to 157 TOPS.
The Mini Series supports various wired and wireless connectivity options through M.2 expansion, including LTE, 4G/5G, Wi-Fi, and Bluetooth.
With built-in NVMe storage and rich input/output options, these systems enable real-time data processing by connecting AI computing directly to field data sources. The fanless design contributes to lower maintenance costs, while the systems’ ability to function with a 12–24V DC power input and within a temperature range of -25°C to +55°C ensures reliability in challenging environments.
By integrating the AIE-CN33/43-A1 with ISP’s gLupe Watcher, the systems can achieve multimodal processing, blending video and natural language inputs to enhance real-time visual recognition, situational understanding, and anomaly detection. This advancement aims to support smart inspection and security monitoring tasks effectively.
