Visual Intelligence for IoT

Updated: Jul 6

It wasn’t that long-ago computer vision applications with AI/ML intelligence required systems with extensive compute capability, lots of memory and high-end industrial cameras. The notion of adding computer vision with ML capability to cost sensitive IoT edge devices was more often than not overshadowed by the reality of high BOM costs which lead to low product margins for all except the most specialized applications. Cloud based solutions provided a possible alternative by mitigating the need for resource rich computational systems at the edge. With cloud based solutions, image processing and ML inferencing could be off-loaded to resource intensive cloud computers. However, the cost of cloud platform instances, security vulnerabilities, and response-time jitter created different obstacles which served as impediments for IoT adoption. Yet, despite all the initial setbacks, computer vision for IoT edge devices is possible. Arm’s release of the high-performance, cost sensitive Cortex-M7 architecture, coupled with the improvements in NN model optimization lay the foundation for computer vision and AI in IoT edge devices. Au-Zone Technologies’ IoT Vision Sensor leverages NXP’s Cortex-M7 based RT1060 with Au-Zone’s highly optimized DeepView ML stack to disrupt the market with a truly cost effective computer vision solution for IoT edge devices.

NXP RT1060

Arm introduced the Cortex-M7 architecture with the intention of enabling developers with IP to create cost-sensitive edge devices. NXP harnessed Arm’s Cortex M7 vision with the release of the NXP RT crossover series which bridges the gap developers often face when confronted with decisions regarding MCU price points vs MPU performance. The RT1060 offers a price point that is commensurate with MCUs; and yet the performance is comparable with modern Arm Cortex-A based MPUs.