What happens when intelligence moves off the cloud and onto the device? Edge AI Studio cuts latency, improves performance, and brings real-time intelligence to the smallest systems.
As we all know, we are living in the era of artificial intelligence (AI). It is everywhere. Let us get straight into how AI, machine learning (ML) and deep learning are shaping our world.
AI is our attempt to mimic human intelligence. On the other hand, ML builds on this by learning patterns from data, while deep learning goes further by using large neural networks and massive datasets. From self-driving cars and machine vision to defect detection, code generation and tools like ChatGPT, the applications are already part of everyday life.
However, what fascinates me most is the evolution of embedded development. Earlier, embedded systems relied heavily on simulators and emulators. Today, AI has changed that workflow entirely. Before an AI model reaches an embedded device, it must be built, tested, refined and only then deployed.
A practical example can be the automated checkout systems in retail. These systems use sensors and vision models to identify products, generate bills and enable fast, queue-free payments. Nobody wants to stand in long checkout lines anymore. For such systems to work, scanning must be fast, accurate and reliable. Edge AI makes this possible by processing data locally, reducing errors and latency while improving customer experience.
How does Edge AI change everything?
This leads to an important question. Why Edge AI? For years, everything depended on the cloud, with increasing latency, bandwidth usage, and operational risk. Running AI directly on devices such as sensors, smartphones, or microcontrollers enables data to be processed instantly at the source.
We have all experienced sensors that fail to respond or doors that open with a delay. Edge AI reduces these frustrations by removing the round-trip to the cloud. It enables faster response times, better performance, and greater reliability, especially for real-time systems.
Edge AI does come with challenges. Embedded systems have limited memory and computing power. Moving from traditional workflows like matrix laboratory (MATLAB) to modern AI pipelines requires a shift in thinking. Bias in models and performance tuning are also real concerns. However, through experimentation, better tools and ongoing research and development, these challenges are becoming manageable.
The real challenge lies in how developers, system designers, and enterprises actually build, optimise, and deploy AI models within tight edge constraints. This is where tooling becomes decisive, turning theoretical advantages into practical, scalable solutions.
Edge AI Studio and its Ecosystem
Edge AI Studio is where these challenges are addressed. It brings together model training, compilation, optimisation, and deployment into a single, seamless workflow. This is where it stands out. If you are already familiar with Python, Hugging Face or GitHub, the transition feels very natural. The tool hub offers trained models, ready-to-use components and complete freedom to customise.
The tool Model Composer is especially powerful. It lets you train and compile models for tasks such as image classification, object detection, semantic segmentation, anomaly detection and more, without writing code.
There is another tool, Model Analyzer, that provides readymade examples while still allowing full customisation. A Model Selection tool helps evaluate options based on latency, accuracy and bandwidth. Model Maker supports a wide range of visual and signal processing use cases. Integrations such as Label Studio make it easy to prepare datasets.
Tools to turn Ideas Into working Intelligence
The tool AI Composer extends these capabilities by handling data capture, data automation, training, compiling and deployment. Compared to the old process of using MATLAB and then writing C or C++ for deployment, this new pipeline accelerates the entire process.
Industries such as agriculture and smart metering are already using this approach through AI-enabled sensors that make real-time decisions. Model Composer also supports full production workflows, from algorithm design and simulation to optimisation and DSP generation. With Simulink, it is easy to work with signal-processing blocks, visual modules, logic components, and data visualisation tools.
Dashboards help compare models, track improvements and tune performance. When needed, custom C or C++ code can still be integrated. One demo showed video frame rotation, which was an excellent example of Video frame rotation using edge computing. Edge refers to performing frame orientation correction directly on edge devices, using hardware accelerators or optimised software to achieve low-latency, power-efficient real-time video processing. The documentation covers everything, including licensing, use guides and tutorials.
One must remember that the Edge AI Studio discussed is not a Barclays product. MATLAB offers AI capabilities, but the tool presented operates independently and supports deployment across embedded platforms. The large-scale updates depend on licensing and deployment strategies. With the appropriate commercial setup, models can be rolled out and updated across devices, even at scale.
By now, it is clear that AI is not just transforming software. It is reshaping the embedded systems landscape. What once relied on fixed logic and rigid rule-based processing is now evolving into a dynamic world where devices can sense, learn and adapt. Edge AI is enabling everyday machines to make instant decisions, understand patterns, process signals and react without waiting for cloud servers.
The article is based on the talk at the EFY Expo in Pune called ‘AI at the Edge: Designing Intelligent Embedded Systems for Real-Time, Cloud-Free Decision Making’ featuring the opening speech by Sangeeta Sinha, AVP, Barclays. It has been transcribed and curated by Saba Aafreen, Technical Journalist at EFY.
