通用神经网络硬件结构优化技术

Jayalakshmi Ravichandran, Radeep Krishna Radhakrishnan Nair
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引用次数: 0

摘要

FPGA具有高并行性、灵活性和低功耗等优点,在数据采样和工业中也有应用。人工智能是实现高效训练和应用神经网络架构的重要领域之一,但对高效训练的要求很高。为了克服这一困难,工业界将FPGA引入人工智能领域。为此,我们倾向于使用处理速度快、硬件资源丰富的XILINX软件在FPGA平台上执行通用神经网络架构并进行初步验证。将大量众多的功能集成在一块芯片上通常具有低紧凑性、低测试必要性、高可靠性、节约成本等优点
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimization Technique for General Neural Network Hardware Architecture
FPGA has the several advantage of highly parallel architecture, flexibility and low power utilization and it also take part in sampling of data and in industries. Artificial intelligence is one of the vital fields to achieve high efficiency for training and apply the neural network architecture but achieving high efficiency is heavily demanded. To overcome this difficulty, industries use FPGA into Artificial intelligence field. For that we tend to execute General neural network Architecture and preliminarily validate on FPGA platform by using XILINX Software which contains high processing speed and abundant hardware resources. The monolithic integration of a large number of numerous functions on a single chip usually provides low compactness, low testing necessity, high reliability, saving cost
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