A. Hiraiwa, M. Fujita, S. Kurosu, S. Arisawa, M. Inoue
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引用次数: 6
摘要
作者提出了一种网格收缩阵列,GCN(千兆连接),用于人工神经网络(ann)的快速模拟器。GCN的处理器单元(PE)由Intel公司设计的RISC处理器i-860、大规模本地存储器和高带宽先进先出器件组成。讨论了神经网络到GCN的映射算法,即网络-数据分区,并利用该算法将多层前馈网络和Kohenen特征映射映射到GCN上。另一种可用于随机人工神经网络的并行性,如玻尔兹曼机,也进行了讨论。通过软件仿真对GCN的性能进行了评估,作者使用128 pe .>实现了每秒超过1千兆的连接
The authors present a mesh systolic array, GCN (giga connection), for a fast simulator of artificial neural networks (ANNs). The processor element (PE) of the GCN is composed of the RISC processor i-860 designed by Intel Corp., a large scale local memory, and high bandwidth first-in first-out devices. The mapping algorithm of the ANN onto the GCN, called the net-data partition, is discussed, and the multilayer feedforward network and Kohenen feature map are mapped onto the GCN by using this algorithm. Another parallelism that can be used for a stochastic ANN like the Boltzmann machine is also discussed. The performance of the GCN is evaluated by software simulation and the authors achieve over 1 gigaconnection per second using 128 PEs.<>