MATLAB’s Role in Shaping the Future of Edge AI
MathWorks is the leading developer of mathematical computing software. MATLAB, the language of engineers and scientists, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. During an interaction with Vidushi, Prashant Rao, Head of Application Engineering, MathWorks India discussed about edge AI Solutions and how MathWorks addresses them.
What current trends and technologies do you see as the primary drivers for the widespread adoption of edge AI across various industries?
The primary drivers for the widespread adoption of edge AI across various industries include the development of more powerful microcontrollers (MCUs) and Digital Signal Processors (DSPs), the use of GPUs for training and running AI models, the introduction of AI Accelerator ASICs such as Neural Processing Units (NPUs), and the advancement of model compression techniques like pruning, quantization, knowledge distillation, and low-rank factorization. These technologies have made edge AI simpler and more economical to implement, enabling real-time inference and decision-making on edge devices without the need for constant internet connectivity.
Edge AI comes with its set of challenges. Could you elaborate on the common challenges associated with implementing edge AI solutions and how MathWorks addresses them?
The common challenges associated with implementing edge AI solutions include the limited computational power and memory constraints of edge devices, the need for efficient model compression without significant loss of accuracy, and the complexity of deploying and integrating AI models into the edge infrastructure. MathWorks addresses these challenges by providing tools and workflows that help engineers choose and train efficient AI models, apply model compression techniques like pruning, projection, and quantization, and generating optimized code (e.g., with Embedded Coder) that can be deployed on edge devices. These tools speed up the development process and help maintain performance while adhering to the hardware limitations of edge devices.
Can you provide examples of edge AI use cases that are applicable and beneficial across multiple industries, showcasing the versatility and impact of this technology?
Examples of edge AI use cases that are beneficial across multiple industries include:
- Automotive Industry: Safety-critical systems, such as Advanced Driver-Assistance Systems (ADAS), use edge AI to process sensor data directly in the vehicle. This allows for immediate actions like automatic braking or lane-keeping, enhancing road safety by adapting to changing conditions in real time.
- Healthcare: In the medical field, edge AI is used in wearable devices and portable diagnostic equipment. For example, Artificial Pancreas (AP) systems monitor and adjust insulin levels for diabetes management. Similarly, portable imaging devices can use AI to analyze scans on-site, aiding in quicker diagnosis.
- Manufacturing: Predictive maintenance powered by edge AI analyzes data from machinery sensors to predict and prevent equipment failures. This minimizes downtime and extends the life of the equipment by addressing issues before they lead to breakdowns.
Given the increasing importance of deploying machine learning and deep learning models on edge devices, how does MATLAB play a role in seamlessly integrating these models to facilitate efficient deployment?
MATLAB facilitates the seamless integration of machine learning and deep learning models on edge devices by providing a platform for engineers to train models using a vast array of toolboxes and then deploy these models directly onto edge devices. MATLAB also offers interoperability with deep learning models designed in other frameworks, such as TensorFlow and PyTorch. With MATLAB Coder, engineers can convert their MATLAB algorithms and accompanying AI models into C/C++ code that can run on the hardware directly, thus bridging the gap between model development and deployment.
In the context of edge AI, what specific features or capabilities does MATLAB offer to support developers and organizations in overcoming the complexities of implementing machine learning and deep learning on edge devices?
MATLAB offers several features and capabilities to support developers and organizations in implementing machine learning and deep learning on edge devices. These include a comprehensive suite of tools for data analysis, algorithm development, and model training, as well as capabilities for model compression and optimization. MATLAB also provides automated code generation, which allows developers to convert MATLAB algorithms into deployable code for edge devices, addressing hardware constraints and facilitating the integration of AI capabilities into edge computing environments.
MATLAB provides a powerful ecosystem for edge AI development, offering tools for data analysis, algorithm creation, and model training. Its model optimization features, including neural network pruning and fixed-point quantization, are crucial for deploying lightweight AI models on resource-constrained edge devices. Additionally, MATLAB’s automated code generation translates algorithms into optimized C/C++/CUDA code, easing the integration of AI into edge environments. These capabilities streamline the process from development to deployment, helping developers overcome the challenges of implementing machine learning and deep learning at the edge.
As industries continue to embrace edge AI, how does MathWorks envision the future of this technology evolving, and what role does the company see itself playing in shaping that future?
Edge AI is a rapidly growing area with significant potential to transform various industries by enabling real-time data processing and decision-making. Advancements in algorithms, sensor technology, and computing power are making it possible to deploy increasingly sophisticated AI models directly onto a wider array of edge devices. MathWorks tools offer a wide range of solutions across multiple application domains covering a wide range of industries and together with the solution aimed at edge AI, engineers can leverage the tools and workflows to implement AI on edge devices across multiple application areas and industries.
Can you share any success stories or notable examples where MathWorks’ solutions have been pivotal in integrating machine learning and deep learning models into edge devices, resulting in tangible benefits for organizations across different sectors?
One notable example where MathWorks solutions have been pivotal is in the automotive industry, where a motor vehicle manufacturer used MATLAB to train a machine-learning model to detect oversteering. The model was deployed in the vehicle’s ECU using MATLAB Coder, enhancing passenger safety by adapting to road conditions in real-time. In the medical device field, a research group developed predictive algorithms for Artificial Pancreas systems using MATLAB, which were deployed on mobile devices to monitor and control glucose levels effectively. These success stories highlight the tangible benefits of integrating machine learning and deep learning models into edge devices using MathWorks solutions.
