HomeElectronics NewsEdge AI And Vision Processing Within An Energy Efficient Framework

Edge AI And Vision Processing Within An Energy Efficient Framework

A Computer-on-Module designed for AI edge functions, these power-saving units enhance industrial automation and vision-focused applications.

Computer on module

Congatec, an embedded and edge computing technology vendor, has launched SMARC Module 2.1 Computer-on-Module based on Texas Instruments Jacinto 7 TDA4x or DRA8x processors. The state-of-the-art industrial-grade modules are suitable for top-tier Artificial Intelligence (AI) edge tasks, boasting an ultra-low power (ULP) footprint, dual Arm Cortex-A72 cores, advanced AI accelerators, and enhanced 3D graphics capabilities. Consuming a mere 5 to 10 watts, the modules are designed for industrial mobile machinery necessitating 2D/3D camera, radar, and lidar analytics. This makes them suitable for automated guided vehicles (AGVs), autonomous mobile robots (AMRs), construction, and farming equipment. Furthermore, they shine in vision-centric industrial automation or medical applications that demand potent yet energy-efficient AI processing on the edge.

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The SMARC modules are equipped with two Mobile Industry Processor Interface (MIPI) Camera Serial Interface (CSI) camera interfaces. The conga-STDA4, powered by the TDA4x processor, incorporates an Image Signal Processor (ISP), vision accelerators, and readily accessible pre-trained AI models from the TI Model Zoo. This facilitates a smooth transition into the realm of AI-driven application design. Moreover, it features a Robotic SDK, amplifying its capabilities. On the other hand, the DRA8x versions present a budget-friendly alternative, lacking the vision processing accelerators. Engineered for rugged industrial conditions, both module versions can operate in extended temperature ranges from -40 °C to +85°C. They also support Time Sensitive Networking (TSN) and incorporate cybersecurity protections.

“By integrating the powerful Texas Instruments JacintoTM 7 TDA4x and TI DRA8x processors in our high-performance SMARC Module 2.1 ecosystem, congatec simplifies the design-in process for this advanced Arm Cortex-A72 based SoC technology. This allows vision-based situational awareness designers in various embedded industries to focus on their core competencies, saving upfront costs and reducing time to market, especially for industrial quantity production”, explains Martin Danzer, Director of Product Management at Congatec.

Industrial OEMs, especially those with limited time and budget for bespoke designs, stand to gain from the SMARC high-performance ecosystem. This system simplifies the design journey, guarantees robust design security, and minimizes one-time engineering (NRE) expenses.

Nidhi Agarwal
Nidhi Agarwal
Nidhi Agarwal is a Senior Technology Journalist at Electronics For You, specialising in embedded systems, development boards, and IoT cloud solutions. With a Master’s degree in Signal Processing, she combines strong technical knowledge with hands-on industry experience to deliver clear, insightful, and application-focused content. Nidhi began her career in engineering roles, working as a Product Engineer at Makerdemy, where she gained practical exposure to IoT systems, development platforms, and real-world implementation challenges. She has also worked as an IoT intern and robotics developer, building a solid foundation in hardware-software integration and emerging technologies. Before transitioning fully into technology journalism, she spent several years in academia as an Assistant Professor and Lecturer, teaching electronics and related subjects. This background reflects in her writing, which is structured, easy to understand, and highly educational for both students and professionals. At Electronics For You, Nidhi covers a wide range of topics including embedded development, cloud-connected devices, and next-generation electronics platforms. Her work focuses on simplifying complex technologies while maintaining technical accuracy, helping engineers, developers, and learners stay updated in a rapidly evolving ecosystem.

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