Edge AI Model Compression and Hardware Acceleration

Abstract

ABSTRACT Edge AI has emerged as a transformative technology that enables machine learning models to execute directly on edge devices such as smartphones, IoT nodes, and embedded systems. However, the deployment of complex AI models at the edge is constrained by limited memory, computational power, and energy resources. Model compression and hardware acceleration techniques provide viable solutions to these challenges by reducing the model size, optimizing computations, and enhancing inference speed. This paper presents a comprehensive review of current model compression strategies, including pruning, quantization, knowledge distillation, and low-rank factorization, alongside state-of-the-art hardware acceleration approaches such as GPUs, TPUs, FPGAs, and custom ASICs. We further discuss the integration of compression techniques with specialized hardware to maximize performance and energy efficiency. Key research challenges, emerging trends, and future directions for Edge AI deployment are also highlighted.

KEYWORDS: Edge AI, Model Compression, Hardware Acceleration, Pruning, Quantization, Knowledge Distillation, FPGA, ASIC, Embedded AI, Energy Efficient Computing