A Comprehensive Review on Hardware Accelerator for YOLO Model Using Sparse Data

Abstract

Object detection plays a crucial role in image processing and data analysis by enabling the extraction of visual information that supports decision-making across diverse practical domains. Applications of object detection span intelligent healthcare, autonomous vehicles, surveillance, robotic vision, and precision agriculture. Acceleration of object detection using convolutional neural networks can be examined from three complementary perspectives: structural, algorithmic, and hardware, which respectively reduce model complexity, optimize computational kernels, and exploit specialized processors. Numerous studies have proposed hardware accelerator architectures to improve the computational efficiency of You Only Look Once object detection models. This paper presents a systematic literature review of hardware accelerators for sparse and compressed YOLO models. The review analyzes how compression techniques, sparsity patterns, convolution implementation strategies, and hardware dataflows interact in YOLO acceleration. It also organizes commonly used evaluation metrics into five groups: sparsity and skipped-data ratio, effective computation reduction, memory and compression efficiency, hardware utilization and workload balance, and system-level efficiency with accuracy trade-off. The findings highlight that sparse data alone does not guarantee acceleration; practical efficiency depends on the co-design of sparsity pattern, convolution mapping, memory hierarchy, and processing-element scheduling. Finally, this review identifies research opportunities in structured sparsity, sparse data movement, processing-element utilization, load balancing, memory aware scheduling, and hardware software co-design for recent YOLO variants.