A survey of neural network accelerators

• Published in: Frontiers of Computer Science 2017-10, Vol.11 (5), p.746-761
• Main Authors: LI, Zhen, WANG, Yuqing, ZHI, Tian, CHEN, Tianshi
• Format: Article
• Language: English
• Subjects: Accelerators; Algorithms; Artificial neural networks; ASICs; Computer Science; DianNao series; FPGAs; Machine learning; Natural language processing; Neural networks; Neurons; Power; Power consumption; Review Article; Speech recognition; 人工神经网络; 人脸识别; 商业应用; 学习技术; 综述; 网络加速器; 自然语言处理; 语音识别
• ISSN: 2095-2228; 2095-2236
• DOI: 10.1007/s11704-016-6159-1

Machine-learning techniques have recently been proved to be successful in various domains, especially in emerging commercial applications. As a set of machine- learning techniques, artificial neural networks （ANNs）, requiring considerable amount of computation and memory, are one of the most popular algorithms and have been applied in a broad range of applications such as speech recognition, face identification, natural language processing, ect. Conventionally, as a straightforward way, conventional CPUs and GPUs are energy-inefficient due to their excessive effort for flexibility. According to the aforementioned situation, in recent years, many researchers have proposed a number of neural network accelerators to achieve high performance and low power consumption. Thus, the main purpose of this literature is to briefly review recent related works, as well as the DianNao-family accelerators. In summary, this review can serve as a reference for hardware researchers in the area of neural networks.