Applications of Embedded System Using AI: A Review

Abstract

Conventional methods for implementing logic programming applications on embedded systems are software- based, requiring a compiler to convert the initial declarative logic program to its equivalent procedural one. This increases the complexity of the final implementation and reduces system performance. However, hardware implementations that only support logic programs prevent their use in applications where logic programs need to be intertwined with traditional procedural ones. This paper exploits HW/SW codesign methods to present a microprocessor capable of supporting hybrid ap- plications using both programming approaches. The proposed implementation is programmable, supports hybrid application execution, increases logic derivation performance, and reduces code complexity. The hardware design is supported by an extended C-language called C-AG. The paper discusses the challenges of high energy consumption and compatibility in deploying artificial intelligence models and networks on embedded devices, introducing methods and applications such as resource-constrained hardware, acceleration methods, neural network compression, and current application models.