Deep neural networks (DNNs) are widely used in AI applications like computer vision, speech recognition, and robotics. While DNNs offer high accuracy, they are computationally intensive, requiring efficient processing to improve energy efficiency and throughput without sacrificing accuracy or increasing hardware costs. This article provides a comprehensive survey of recent advances in DNN processing, covering DNN fundamentals, hardware platforms, optimization techniques, and benchmarking metrics. It discusses key trends in reducing computational costs through hardware and algorithmic changes, and highlights development resources for researchers and practitioners. The article also outlines important design considerations for evaluating DNN hardware implementations. DNNs are a subset of AI, inspired by the brain's structure and function. They use layered networks to extract high-level features from data, enabling superior performance in tasks like image recognition and speech processing. However, their high computational complexity requires efficient processing. Training DNNs involves adjusting weights to minimize loss, while inference involves using trained weights to make predictions. DNNs have been successful in various applications, including image and video processing, speech recognition, medical imaging, game playing, and robotics. The development of DNNs has been driven by advances in data availability, computational power, and algorithmic techniques. Notable DNN models include LeNet, AlexNet, VGG-16, GoogLeNet, and ResNet, each with varying numbers of layers and architectures. These models have achieved high accuracy in tasks like image classification, with ResNet being the first to exceed human-level performance. DNNs are now used in a wide range of applications, from healthcare to finance, and their efficient processing is critical for deployment in resource-constrained environments like embedded systems. This article discusses the importance of efficient DNN processing, focusing on inference rather than training, and highlights key hardware and algorithmic optimizations for improving performance. It also covers the development resources available for DNN research, including frameworks like Caffe, TensorFlow, and Keras, as well as pretrained models and datasets for classification tasks. The article emphasizes the need for adaptive and scalable solutions to handle the diverse forms of DNNs in various applications.Deep neural networks (DNNs) are widely used in AI applications like computer vision, speech recognition, and robotics. While DNNs offer high accuracy, they are computationally intensive, requiring efficient processing to improve energy efficiency and throughput without sacrificing accuracy or increasing hardware costs. This article provides a comprehensive survey of recent advances in DNN processing, covering DNN fundamentals, hardware platforms, optimization techniques, and benchmarking metrics. It discusses key trends in reducing computational costs through hardware and algorithmic changes, and highlights development resources for researchers and practitioners. The article also outlines important design considerations for evaluating DNN hardware implementations. DNNs are a subset of AI, inspired by the brain's structure and function. They use layered networks to extract high-level features from data, enabling superior performance in tasks like image recognition and speech processing. However, their high computational complexity requires efficient processing. Training DNNs involves adjusting weights to minimize loss, while inference involves using trained weights to make predictions. DNNs have been successful in various applications, including image and video processing, speech recognition, medical imaging, game playing, and robotics. The development of DNNs has been driven by advances in data availability, computational power, and algorithmic techniques. Notable DNN models include LeNet, AlexNet, VGG-16, GoogLeNet, and ResNet, each with varying numbers of layers and architectures. These models have achieved high accuracy in tasks like image classification, with ResNet being the first to exceed human-level performance. DNNs are now used in a wide range of applications, from healthcare to finance, and their efficient processing is critical for deployment in resource-constrained environments like embedded systems. This article discusses the importance of efficient DNN processing, focusing on inference rather than training, and highlights key hardware and algorithmic optimizations for improving performance. It also covers the development resources available for DNN research, including frameworks like Caffe, TensorFlow, and Keras, as well as pretrained models and datasets for classification tasks. The article emphasizes the need for adaptive and scalable solutions to handle the diverse forms of DNNs in various applications.