May 30, 2023
blog
Embedded platforms have become an integral part of our daily lives and have revolutionized the way we interact with technology. Powered by deep learning algorithms, these platforms open up a world of possibilities, enabling smart devices, autonomous systems and intelligent applications. Deploying deep learning algorithms on embedded platforms is very important.
It involves the process of optimizing and adapting deep learning models to run efficiently on resource-constrained embedded systems such as microcontrollers, FPGAs, and CPUs. This deployment process often requires model compression, quantization, and other techniques to reduce model size and computational requirements without sacrificing performance.
The global market for embedded systems is growing rapidly and is expected to reach US$170.04 billion by 2023. It is expected to continue its upward trajectory, reaching approximately US$258.6 billion by 2032, according to previous research studies. The compound annual growth rate (CAGR) for the period 2023-2032 is around 4.77%.
Market analysis gives us some key insights. In 2022, North America emerged as the dominant region with her 51% of total revenue share, while Asia Pacific accounted for her sizeable share of 24%. In terms of hardware platforms, the ASIC segment grabbed a significant market share of 31.5%, while the microprocessor segment took his 22.3% of his 2022 revenue share.
Embedded platforms have limited memory, processing power, and energy resources compared to traditional computing systems. Therefore, deploying deep learning algorithms on these platforms requires careful consideration of hardware constraints and trade-offs between accuracy and resource utilization.
Deployment involves converting a trained deep learning model into a format compatible with the target embedded platform. This includes converting models to framework-specific formats or optimizing models for specific hardware accelerators or libraries.
Additionally, deploying deep learning algorithms on embedded platforms often requires leveraging hardware acceleration techniques such as GPU acceleration, specialized neural network accelerators, or custom hardware designs such as FPGAs and ASICs. I have.
These hardware accelerators can significantly improve the inference speed and energy efficiency of deep learning algorithms on embedded platforms. Deploying deep learning algorithms on embedded platforms typically involves:
(deployment of deep learning models to various embedded platforms)
Bringing deep learning algorithms to embedded platforms requires careful optimization and adaptation. Model compression, quantization, and pruning techniques can reduce model size and computational requirements without compromising performance.
Understanding the unique hardware constraints of embedded platforms is critical to a successful deployment. Factors such as available memory, processing power, and energy limitations should be carefully analyzed. Choosing a deep learning model and architecture that effectively utilizes the resources of the target embedded platform is essential for optimal performance and efficiency.
Converting a trained deep learning model into a format compatible with embedded platforms is an important step in the deployment process. Framework-specific formats such as TensorFlow Lite and ONNX are commonly used. Additionally, adapting the model to take advantage of specialized hardware accelerators such as GPUs, neural network accelerators, or custom designs such as FPGAs and ASICs can significantly improve inference speed and energy efficiency on embedded platforms. can.
Real-time performance and low latency are very important in the area of embedded systems. A deep learning algorithm must meet the timing requirements of a particular application to ensure fast and efficient execution of the inference process. Balancing the limited resources and real-time demands of embedded platforms requires careful optimization and fine-tuning.
An iterative refinement process may be required if the deployed model does not meet the desired performance or resource constraints. This may include further model optimization, hardware tuning, or algorithm modifications to improve the performance or efficiency of the deployed deep learning algorithms.
Several frameworks and tools have emerged to facilitate the deployment of deep learning algorithms on embedded platforms. TensorFlow Lite, PyTorch Mobile, Caffe2, OpenVINO, and ARM CMSIS-NN libraries are among the popular choices, providing optimized libraries and runtime environments to run efficiently on embedded devices.
Let’s look at some use cases where deploying deep learning models on embedded edge platforms is a good fit.
- Self-driving cars: Self-driving cars rely heavily on computer vision algorithms trained using deep learning techniques such as convolutional neural networks (CNN) and recurrent neural networks (RNN). These systems process images from cameras mounted on self-driving cars to detect objects such as pedestrians crossing the road, cars parked along the curb, and cyclists. , based on which the self-driving car performs actions.
- Healthcare and Remote Monitoring: Healthcare: Deep learning is rapidly gaining traction in the healthcare industry. For example, wearable sensors and devices utilize patient data to provide real-time insights into various health metrics, including overall health, blood sugar levels, blood pressure, heart rate, and more. These technologies leverage deep learning algorithms to analyze and interpret the collected data, providing valuable information for monitoring and managing patient conditions.
In the future, we can expect exciting progress in deploying deep learning algorithms to embedded platforms. The integration of deep learning and Internet of Things (IoT) devices opens up even more possibilities for embedded AI. Custom hardware designs tailored to deep learning algorithms on embedded platforms are also expected, improving efficiency and performance.
Deploying deep learning algorithms on embedded platforms requires a structured process to optimize models, consider hardware constraints, and address real-time performance requirements. By following this process, businesses can harness the power of AI in resource-constrained systems to drive innovation, streamline operations, and deliver superior products and services. Embracing this technology will unlock new possibilities for businesses, leading to sustainable growth and success in today’s AI-driven world.
Real-time performance requirements and latency constraints are important considerations when deploying deep learning algorithms on embedded platforms, and efficient execution of the inference process depends on it.
Ambuj Nandanwar is a marketing expert at Softnautics, writing impactful techno-commercial articles and conducting extensive market research to promote your business on various platforms. He has been a dedicated marketer for over two years and is always looking for new initiatives. When not working, Ambuj enjoys biking and exploring new destinations.
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