Neural network (NN) processors are specially designed to handle deep learning tasks by utilizing multilayer artificial NNs. They have been demonstrated to be useful in broad application fields such as image recognition, speech processing, machine translation, and scientific computing. Meanwhile, innovative self-aware techniques, whereby a system can dynamically react based on continuously sensed information from the execution environment, have attracted attention from both academia and industry. Actually, various self-aware techniques have been applied to NN systems to significantly improve the computational speed and energy efficiency. This article surveys state-of-the-art self-aware NN systems (SaNNSs), which can be achieved at different layers, that is, the architectural layer, the physical layer, and the circuit layer. At the architectural layer, SaNNS can be characterized from a data-centric perspective where different data properties (i.e., data value, data precision, dataflow, and data distribution) are exploited. At the physical layer, various parameters of physical implementation are considered. At the circuit layer, different logics and devices can be used for high efficiency. In fact, the self-awareness of existing SaNNS is still in a preliminary form. We propose a comprehensive SaNNS from a new perspective, that is, the model layer, to exploit more opportunities for high efficiency. The proposed system is called as MinMaxNN, which features model switching and elastic sparsity based on monitored information from the execution environment. The model switching mechanism implies that models (i.e., min and max model) dynamically switch given different inputs for both efficiency and accuracy. The elastic sparsity mechanism indicates that the sparsity of NNs can be dynamically adjusted in each layer for efficiency. The experimental results show that compared with traditional SaNNS, MinMaxNN can achieve $5.64\times $ and 19.66% performance improvement and energy reduction, respectively, without notable loss of accuracy and negative effects on developers’ productivity.

中文翻译：

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自我意识神经网络系统：调查和新视角

神经网络 (NN) 处理器专门设计用于利用多层人工 NN 处理深度学习任务。它们已被证明在广泛的应用领域很有用，例如图像识别、语音处理、机器翻译和科学计算。同时，创新的自我意识技术，即系统可以根据来自执行环境的持续感知信息动态地做出反应，引起了学术界和工业界的关注。实际上，各种自感知技术已应用于神经网络系统，以显着提高计算速度和能源效率。本文调查了最先进的自感知神经网络系统 (SaNNSs)，它们可以在不同层实现，即架构层、物理层和电路层。在架构层，SaNNS 可以从以数据为中心的角度进行表征，其中利用了不同的数据属性（即数据值、数据精度、数据流和数据分布）。在物理层，考虑了物理实现的各种参数。在电路层，可以使用不同的逻辑和器件来实现高效率。事实上，现有的 SaNNS 的自我意识还处于初步形式。我们从一个新的角度，即模型层，提出了一个全面的 SaNNS，以利用更多的机会来提高效率。所提出的系统称为 MinMaxNN，它具有基于来自执行环境的监控信息的模型切换和弹性稀疏性。模型切换机制意味着模型（即，最小和最大模型）动态切换给定不同的输入以提高效率和准确性。弹性稀疏机制表明神经网络的稀疏性可以在每一层动态调整以提高效率。实验结果表明，与传统的 SaNNS 相比，MinMaxNN 可以分别实现 $5.64\times $ 和 19.66% 的性能提升和能耗降低，并且没有显着的准确性损失和对开发人员生产力的负面影响。