AIOMs: The State of the Art in SMARCs
The past few years have seen an explosive growth in the use of artificial intelligence (AI) and machine learning (ML) and the market for AI-enabled products continues to grow exponentially.
Examples of AI-enabled systems include robots, predictive maintenance systems, and intelligent surveillance systems. In the case of robots, the latest generation of industrial robots are known as autonomous mobile robots (AMRs). The fusion of technologies like computer vision, GPS, and AI allows AMRs to perform object detection and recognition and navigate their way around an uncontrolled environment in which the landscape may be constantly changing.
With regard to predictive maintenance applications, systems equipped with AI can monitor things like sounds and vibrations to determine the health of machines. When these AI systems detect anomalies and observe undesirable trends, they can alert their human colleagues as to impending problems, thereby allowing maintenance teams to address these issues without disrupting the operations of the factory or facility.
In the case of surveillance and monitoring applications, smart cameras can be used to detect and recognize people and objects in factories, cities, and homes.
Although AI applications can run on a variety of computing platforms – including microprocessor units (MPUs) and graphics processing units (GPUs) -- tremendous gains in performance accompanied by dramatic reductions in power can be achieved by employing special neural processing units (NPUs). These 21st century processing engines implement all the necessary control and arithmetic logic necessary to execute AI and ML algorithms in the most efficient manner possibly.
The problem with using traditional SMARC modules with standalone NPUs is that designers also need to create the associated carrier boards in such a way that they can accommodate add-on cards carrying the NPU. In addition to increasing the size, cost, and power of the resulting system, this approach negatively impacts development time, effort, and resources, reduces time-to-market, and increases risk.
The solution is to use a SMARC AIOM (AI-on-Module), which is a SMARC COM that carries both MPU and NPU devices.