Efficient Processing of Deep Neural Networks. Vivienne Sze
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Essentially, the goal is to find the structure or clusters in the data. Semi-supervised learning falls between the two approaches, where only a small subset of the training data is labeled (e.g., use unlabeled data to define the cluster boundaries, and use the small amount of labeled data to label the clusters). Finally, reinforcement learning can be used to the train the weights such that given the state of the current environment, the DNN can output what action the agent should take next to maximize expected rewards; however, the rewards might not be available immediately after an action, but instead only after a series of actions (often referred to as an episode).
Another commonly used approach to determine weights is fine-tuning, where previously trained weights are available and are used as a starting point and then those weights are adjusted for a new dataset (e.g., transfer learning) or for a new constraint (e.g., reduced precision). This results in faster training than starting from a random starting point, and can sometimes result in better accuracy.
This book will focus on the efficient processing of DNN inference rather than training, since DNN inference is often performed on embedded devices (rather than the cloud) where resources are limited, as discussed in more details later.
1.3 DEVELOPMENT HISTORY
Although neural networks were proposed in the 1940s, the first practical application employing multiple digital neurons didn’t appear until the late 1980s, with the LeNet network for handwritten digit recognition [20].7 Such systems are widely used by ATMs for digit recognition on checks. The early 2010s have seen a blossoming of DNN-based applications, with highlights such as Microsoft’s speech recognition system in 2011 [6] and the AlexNet DNN for image recognition in 2012 [7]. A brief chronology of deep learning is shown in Figure 1.7.
The deep learning successes of the early 2010s are believed to be due to a confluence of three factors. The first factor is the amount of available information to train the networks. To learn a powerful representation (rather than using a hand-crafted approach) requires a large amount of training data. For example, Facebook receives up to a billion images per day, Walmart creates 2.5 Petabytes of customer data hourly and YouTube has over 300 hours of video uploaded every minute. As a result, these and many other businesses have a huge amount of data to train their algorithms.
The second factor is the amount of compute capacity available. Semiconductor device and computer architecture advances have continued to provide increased computing capability, and we appear to have crossed a threshold where the large amount of weighted sum computation in DNNs, which is required for both inference and training, can be performed in a reasonable amount of time.
Figure 1.7: A concise history of neural networks. “Deep” refers to the number of layers in the network.
The successes of these early DNN applications opened the floodgates of algorithmic development. It has also inspired the development of several (largely open source) frameworks that make it even easier for researchers and practitioners to explore and use DNNs. Combining these efforts contributes to the third factor, which is the evolution of the algorithmic techniques that have improved accuracy significantly and broadened the domains to which DNNs are being applied.
An excellent example of the successes in deep learning can be illustrated with the ImageNet Challenge [23]. This challenge is a contest involving several different components. One of the components is an image classification task, where algorithms are given an image and they must identify what is in the image, as shown in Figure 1.5. The training set consists of 1.2 million images, each of which is labeled with one of a thousand object categories that the image contains. For the evaluation phase, the algorithm must accurately identify objects in a test set of images, which it hasn’t previously seen.
Figure 1.8 shows the performance of the best entrants in the ImageNet contest over a number of years. The accuracy of the algorithms initially had an error rate of 25% or more. In 2012, a group from the University of Toronto used graphics processing units (GPUs) for their high compute capability and a DNN approach, named AlexNet, and reduced the error rate by approximately 10 percentage points [7]. Their accomplishment inspired an outpouring of deep learning algorithms that have resulted in a steady stream of improvements.
In conjunction with the trend toward using deep learning approaches for the ImageNet Challenge, there has been a corresponding increase in the number of entrants using GPUs: from 2012 when only 4 entrants used GPUs to 2014 when almost all the entrants (110) were using them. This use of GPUs reflects the almost complete switch from traditional computer vision approaches to deep learning-based approaches for the competition.
Figure 1.8: Results from the ImageNet Challenge [23].
In 2015, the ImageNet winning entry, ResNet [24], exceeded human-level accuracy with a Top-5 error rate8 below 5%. Since then, the error rate has dropped below 3% and more focus is now being placed on more challenging components of the competition, such as object detection and localization. These successes are clearly a contributing factor to the wide range of applications to which DNNs are being applied.
1.4 APPLICATIONS OF DNNs
Many domains can benefit from DNNs, ranging from entertainment to medicine. In this section, we will provide examples of areas where DNNs are currently making an impact and highlight emerging areas where DNNs may make an impact in the future.
• Image and Video: Video is arguably the biggest of big data. It accounts for over 70% of today’s Internet traffic [25]. For instance, over 800 million hours of video is collected daily worldwide for video surveillance [26]. Computer vision is necessary to extract meaningful information from video. DNNs have significantly improved the accuracy of many computer vision tasks such as image classification [23], object localization and detection [27], image segmentation [28], and action recognition [29].
• Speech and Language: DNNs have significantly improved the accuracy of speech recognition [30] as well as many related tasks such as machine translation [6], natural language processing [31], and audio generation [32].
• Medicine and Health Care: DNNs have played an important role in genomics to gain insight into the genetics of diseases such as autism, cancers, and spinal muscular atrophy [33–36]. They have also been used in medical imaging such as detecting skin cancer [9], brain cancer [37], and breast cancer [38].
• Game Play: Recently, many of the grand AI challenges involving game play have been overcome using DNNs. These successes also required innovations in training techniques, and many rely on reinforcement learning [39]. DNNs have surpassed human level accuracy in playing games such as Atari [40], Go [10], and StarCraft [41], where an exhaustive search of all possibilities is not feasible due to the immense number of possible moves.
• Robotics: DNNs have been successful in the domain of robotic tasks such as grasping with a robotic arm [42], motion planning for ground robots [43], visual navigation [8, 44], control to stabilize a quadcopter [45], and driving strategies for autonomous vehicles [46].
DNNs are already widely used in multimedia applications today (e.g., computer vision, speech recognition). Looking forward, we expect that DNNs will likely play an increasingly important role in the medical and robotics fields, as discussed above, as well as finance (e.g., for trading, energy forecasting, and risk assessment), infrastructure (e.g., structural safety, and traffic control), weather forecasting, and event detection [47]. The myriad application domains pose new challenges to the efficient processing of DNNs; the solutions then have