"Connected Future Equals Internet of Things, Artificial Intelligence, and Machine Learning"
The world of Artificial Intelligence (AI) is evolving, and one of the most exciting developments is the rise of multimodal AI. This innovative approach involves using multiple AI systems simultaneously, promising significant advancements for autonomous systems.
In the realm of physically autonomous systems, such as autonomous mobile robots, the use of multimodal AI is particularly promising. Currently, these systems often consist of separate subsystems with limited interaction. However, by embracing multimodal AI, they can increase their autonomy, processing diverse sensor inputs like LIDAR, radar, visual cameras, and audio signals more effectively.
The integration of AI into these systems is often achieved at the edge, a term referring to the devices themselves rather than the cloud. This is where machine learning (ML) is typically run on microcontrollers, and some manufacturers are now embedding AI or ML directly into sensors.
As AI moves into the early majority stage, thanks to multimodal AI, it is becoming more accessible to a wider audience. Generative AI is a significant focus for many in the industry, with the next step being to make edge-based systems multimodal.
However, achieving viable multimodal AI presents challenges. AI models are usually designed to operate on one type of data. To overcome this, methods such as developing models trained on more than one data type, running multiple models, developing simpler models, cascading multiple AI or ML models, and combining multiple hardware-based ML solutions are being explored.
The implementation of multimodal AI at the edge requires a combination of lightweight multimodal AI models tailored for edge constraints, hardware platforms combining application CPUs, microcontrollers, and dedicated AI accelerators, and software frameworks enabling optimized model deployment and real-time multimodal data processing.
Leading semiconductor companies supply SoCs (system on chips) featuring multiple cores and accelerators tailored for edge AI. On the software side, development environments, model optimization tools, edge AI runtime frameworks, and integration libraries are all crucial for enabling multimodal AI at the edge.
The demand for deploying multimodal AI at the edge is growing, and businesses are recognising the commercial opportunities. Middleware platforms like IOTCONNECT on AWS provide the cloud infrastructure for multimodal AI. Distributors are working with suppliers to integrate AI and machine learning models into pre-compiled demonstration platforms, supporting their customers through these advances.
With over 1.8 million AI models available on platforms like Hugging Face, and models typically available for free from repositories such as GitHub, the electronics industry is reacting to the rapid expansion of AI and machine learning. Cascading AI or ML models is similar to how some microcontrollers use autonomous peripherals to monitor hardware, reducing system power without sacrificing performance.
In a true multimodal AI system, separate single-modality AI models would process different types of sensor data, and a third model would understand their outputs. This enables autonomous systems to perform complex perception tasks locally and respond in real time without reliance on cloud connectivity, crucial for safety and latency-sensitive applications.
As we move forward, the integration of machine learning into embedded and connected systems will become increasingly important. The momentum created by AI continues to gain speed, and the industry is prepared to support its customers through these exciting advances.
[1] A Survey on Edge AI for Autonomous Systems [2] Edge AI: Enabling Autonomous Systems with On-Device Machine Learning [3] Edge AI: Enabling Autonomous Systems with On-Device Machine Learning [4] Developing Edge AI for Autonomous Systems
- In the realm of edge AI, the integration of lightweight multimodal AI models with hardware platforms and software frameworks is vital for effectively processing diverse sensor inputs in autonomous systems.
- As AI and machine learning continue to expand, the electronics industry is exploring various methods such as cascading AI or ML models to create more sophisticated, multimodal AI systems, enabling autonomous systems to perform complex perception tasks locally and respond in real-time.
