Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2017 Aug 23;17(9):1941. doi: 10.3390/s17091941.
Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas.
人工神经网络（ANNs），包括深度神经网络（DNNs），已经成为机器学习的最新方法，并在语音识别、视觉目标识别和许多其他领域取得了惊人的成功。有几个硬件平台可用于开发 ANN 模型的加速实现。由于现场可编程门阵列（FPGA）架构具有灵活性并且可以提供每瓦功耗的高性能，因此它们已经吸引了许多科学家的应用。在本文中，我们提出了一种基于 FPGA 的、粒度可变的神经形态处理器（FBGVNP）。FBGVNP 的特点可以概括为粒度可变性、可扩展性、集成计算和寻址能力：首先，一个核中的神经元数量是可变的，而不是固定的；其次，多核网络的规模可以以各种形式扩展；第三，神经元寻址和计算过程是同时执行的。这使得处理器更加灵活，更适合不同的应用。此外，基于神经网络的控制器被映射到 FBGVNP 上，并应用于多输入、多输出（MIMO）实时、温度感应和控制系统。实验验证了神经形态处理器的有效性。FBGVNP 为构建 ANN 提供了一种灵活、高能效的新方案，可应用于许多领域。
