Exploring Neural Network Compression For Deep Learning Inference In Embedded Systems

ABSTRACT Deep learning has transformed artificial intelligence applications, but applying big neural networks on limited-resource embedded systems is still a major challenge. Compression techniques for neural networks have become a key solution to maximize inference efficiency without sacrificing accuracy. This paper discusses several compression approaches, such as pruning, quantization, knowledge distillation, and low-rank factorization, to improve deep learning inference performance on embedded systems. The work presents a detailed analysis of model size vs. computational complexity vs. accuracy trade-offs and the real-world implications of applying compressed networks in real-time settings. The paper also addresses hardware constraint, energy efficiency, and edge AI platform implementation challenges. Performance benchmarking and case studies illustrate the effectiveness of various compression techniques in accelerating inference speed, memory footprint reduction, and power optimization. The paper concludes by examining future directions in automated AI based compression methods, federated learning, and neuromorphic computing, mapping out the possibilities for further innovations in efficient deep learning inference for embedded systems.