دانلود کتاب شتاب دهنده های شبکه های عصبی کانولوشنال

Deep neural networks (DNNs) have enabled the deployment of artificial intelligence (AI) in many modern applications including autonomous driving [1], image recognition [2], and speech processing [3]. In many applications, DNNs have achieved close to human-level accuracy and, in some, they have exceeded human accuracy [4]. This high accuracy comes from a DNN’s unique ability to automatically extract high-level features from a huge quantity of training data using statistical learning and improvement over time. This learning over time provides a DNN with an effective representation of the input space. This is quite different from the earlier approaches where specific features were hand-crafted by domain experts and were subsequently used for feature extraction. Convolutional neural networks (CNNs) are a type of DNNs, which are most commonly used for computer vision tasks. Among different types of DNNs, such as multilayer perceptrons (MLP), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, radial basis function networks (RBFNs), generative adversarial networks (GANs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), and autoencoders, CNNs are the mostly commonly used. Invention of CNNs has revolutionized the field of computer vision and has enabled many applications of computer vision to go mainstream. CNNs have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, object detection, activity recognition, natural language processing, brain–computer interfaces, and financial time-series prediction. DNN/CNN processing is usually carried out in two stages, training and inference, with both of them having their own computational needs. Training is the process where a DNN model is trained using a large application-specific data set. The training time is dependent on the model size and the target accuracy requirements. For high accuracy applications like autonomous driving, training a DNN can take weeks and is usually performed on a cloud. Inference, on the other hand, can be performed either on the cloud or the edge device (mobile device, Internet of things (IoT), autonomous vehicle, etc.). Nowadays, in many applications, it is advantageous to perform the inference process on the edge devices, as shown in Figure 1.1. For example, in cellphones, it is desirable to perform image and video processing on the device itself rather than sending the data over to the cloud for processing. This methodology reduces the communication cost and the latency involved with the data transmission and reception. It also eliminates the risk of losing important device features should there be a network disruption or loss of connectivity. Anothermotivation for doing inference on the device is the ever-increasing security risk involved with sending personalized data, including images and videos, over to the cloud servers for processing. Autonomous driving systems which require visual data need to deploy solutions to perform inference locally to avoid latency and security issues, both of which can result in a catastrophe, should an undesirable event occurs. Performing DNN/CNN inference on the edge presents its own set of challenges. This stems from the fact that the embedded platforms running on the edge devices have stringent cost limitations which limit their compute capabilities. Running compute and memory-intensive DNN/CNN inference in these devices in an efficient manner becomes a matter of prime importance.