Abstract
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.
Highlights
In recent years, machine learning has entered into our daily life
When General Purpose Processors (GPPs) execute a parallel based on multiple cores, specially designed Application Specific IntegratedCircuits (ASICs) and designed ASICs and Field Programmable Gate Array (FPGA) can support inherently pipelined and multithreaded applications, which
A FCPIDNN is mapped to the neuromorphic processor and applied in a temperature
Summary
Machine learning has entered into our daily life. When we communicate with smart phones using natural language or get pictures on digital cameras using face detection, artificial intelligence plays a key role in the process [1]. The SpiNNaker machine is a designed computer for supporting the sorts of communication found in the brain It is based on the connection of processing nodes, which have eighteen ARM processor cores in one node. The bottleneck which serves as the networks in a von Neumann architecture computer, a central processor has to simulate communication communication channel between the processor and external memory causes power-hungry data infrastructure and a great number of neurons. When GPPs execute a parallel based on multiple cores, specially designed ASICs and designed ASICs and FPGAs can support inherently pipelined and multithreaded applications, which. General Purpose Processors (GPPs) multicore CPU computing process computing structure energy-efficiency development round cost. Granularity variability: The number of the cells in one neural computing unit can vary, which will enhance the flexibility of the neuromorphic core compared with fixed architectures.
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