Fully Connected Neural Network (FCNN) is a class of Artificial Neural Networks widely used in computer science and engineering, whereas the training process can take a long time with large datasets in existing many-core systems. Optical Network-on-Chip (ONoC), an emerging chip-scale optical interconnection technology, has great potential to accelerate the training of FCNN with low transmission delay, low power consumption, and high throughput. However, existing methods based on Electrical Network-on-Chip (ENoC) cannot fit in ONoC because of the unique properties of ONoC. In this paper, we propose a fine-grained parallel computing model for accelerating FCNN training on ONoC and derive the optimal number of cores for each execution stage with the objective of minimizing the total amount of time to complete one epoch of FCNN training. To allocate the optimal number of cores for each execution stage, we present three mapping strategies and compare their advantages and disadvantages in terms of hotspot level, memory requirement, and state transitions. Simulation results show that the average prediction error for the optimal number of cores in NN benchmarks is within 2.3%. We further carry out extensive simulations which demonstrate that FCNN training time can be reduced by 22.28% and 4.91% on average using our proposed scheme, compared with traditional parallel computing methods that either allocate a fixed number of cores or allocate as many cores as possible, respectively. Compared with ENoC, simulation results show that under batch sizes of 64 and 128, on average ONoC can achieve 21.02% and 12.95% on reducing training time with 47.85% and 39.27% on saving energy, respectively.

翻译：47. 然而,基于电子网络芯片(ENOC)的现有方法无法在计算机科学和工程中广泛使用,而培训过程则需要很长的时间,因为现有多核心系统中有大量数据集。光学网络在芯片上(ONOC)是一个新兴的芯片规模光学互联技术,极有可能以低传输延迟、低电耗和高吞吐量加速对FCNN的培训。然而,基于电子网络芯片(ENOC)的现有方法无法适应ONoC。由于ONoC的独特性能,因此在计算机科学和工程中广泛使用人工智能系统。在本文中,我们提出一个精细的平行计算模型,用于加速FCNN在现有的多核心系统中的大型数据集。为了最大限度地减少完成FCNN培训的总数,我们提出了三种绘图策略,并比较其在热点水平、记忆要求和状态转型方面的优劣之处。在本文中,我们提出了一套精细的平行计算模型,用于加速FNCNC培训的精度(ERN)的精度和优度(EN)的精度(EN)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)(O)(O)(O)的精度(O)(O),在4.),在4.),在4)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)的精度(O)(O)(O)(O)(O)(O)(O)(O)(O)(O)(O)(O)(O)(O)中)中)(O)(O)(O)(O)(O)(O)(O)(O)(O)(O)