📚 Understanding the Deployment of Deep Learning Algorithms on Embedded Platforms

Embedded platforms have become an integral part of our daily lives, revolutionizing our technological interaction. These platforms, equipped with deep learning algorithms, have opened a world of possibilities, enabling smart devices, autonomous systems, and intelligent applications. Therefore, the deployment of deep learning algorithms on embedded platforms is crucial. 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 the model size and computational requirements without sacrificing performance.

The global market for embedded systems has experienced rapid expansion, expected to reach USD 170.04 billion in 2023. As per the precedence research survey, it is expected to continue its upward trajectory, with estimates projecting it to reach approximately USD 258.6 billion by 2032. The forecasted Compound Annual Growth Rate (CAGR) during the period from 2023 to 2032 is around 4.77%. Several key insights emerge from the market analysis. In 2022, North America emerged as the dominant region, accounting for 51% of the total revenue share, while Asia Pacific held a considerable share of 24%. In terms of hardware platforms, the ASIC segment had a substantial market share of 31.5%, and the microprocessor segment captured 22.3% of the revenue share in 2022.