摘要:
近年来，人工智能技术在许多商业领域获得了广泛应用，并且随着世界各地的科研人员和科研公司的重视和投入，人工智能技术在传统语音识别、图像识别、搜索/推荐引擎等领域证明了其不可取代的价值.但与此同时，人工智能技术的运算量也急剧扩增，给硬件设备的算力提出了巨大的挑战.从人工智能的基础算法以及其应用算法着手，描述了其运算方式及其运算特性.然后，介绍了近期人工智能芯片的发展方向，对目前智能芯片的主要架构进行了介绍和分析.而后，着重介绍了DianNao系列处理器的研究成果.该系列的处理器为智能芯片领域最新最先进的研究成果，其结构和设计分别面向不同的技术特征而提出，包括深度学习算法、大规模的深度学习算法、机器学习算法、用于处理二维图像的深度学习算法以及稀疏深度学习算法等.此外，还提出并设计了完备且高效的Cambricon指令集结构.最后，对人工神经网络技术的发展方向从多个角度进行了分析，包括网络结构、运算特性和硬件器件等，并基于此对未来工作可能的发展方向进行了预估和展望.
Abstract:
In recent years, artificial intelligence （AI）technologies have been widely used in many commercial fields. With the attention and investment of scientific researchers and research companies around the world, AI technologies have been proved their irreplaceable value in traditional speech recognition, image recognition, search/recommendation engine and other fields. However, at the same time, the amount of computation of AI technologies increases dramatically, which poses a huge challenge to the computing power of hardware equipments. At first, we describe the basic algorithms of AI technologies and their application algorithms in this paper, including their operation modes and operation characteristics. Then, we introduce the development directions of AI chips in recent years, and analyze the main architectures of AI chips. Furthermore, we emphatically introduce the researches of DianNao series processors. This series of processors are the latest and most advanced researches in the field of AI chips. Their architectures and designs are proposed for different technical features, including deep learning algorithms, large-scale deep learning algorithms, machine learning algorithms, deep learning algorithms for processing two-dimensional images and sparse deep learning algorithms. In addition, a complete and efficient instruction architecture(ISA) for deep learning algorithms, Cambricon, is proposed. Finally, we analyze the development directions of artificial neural network technologies from various angles, including network structures, operation characteristics and hardware devices. Based on the above, we predict and prospect the possible development directions of future work.
百度学术
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