AI Model Optimization Techniques
G. Victor Daniel, Mandhula Trupthi, G. Sridhar Reddy, A. Mallikarjuna Reddy, Kosari Hemanth Sai · 2024
This chapter provides a comprehensive overview of model optimization techniques in the field of deep learning. It explores various methods and strategies to enhance the efficiency and practicality of deep neural networks, covering pruning, quantization, model distillation, layer fusion, parallelization, hardware acceleration, transfer learning, neural architecture search, and pragmatic optimization. The chapter discusses the fundamentals of pruning, quantization techniques, and the benefits of model distillation, emphasizing their real-world applications. It also delves into layer fusion and parallelization approaches, along with the role of hardware acceleration and its optimization for specific hardware. Transfer learning, neural architecture search, and pragmatic optimization are thoroughly examined, highlighting their benefits and real-world applications. This comprehensive overview serves as a valuable resource for researchers, practitioners, and enthusiasts in the field of machine learning and artificial intelligence.