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Recently, optical neural networks (ONNs) integrated in photonic chips has received extensive attention because they are expected to implement the same pattern recognition tasks in the electronic platforms with high efficiency and low power consumption. However, the current lack of various learning algorithms to train the ONNs obstructs their further development. In this article, we propose a novel learning strategy based on neuroevolution to design and train the ONNs. Two typical neuroevolution algorithms are used to determine the hyper-parameters of the ONNs and to optimize the weights (phase shifters) in the connections. In order to demonstrate the effectiveness of the training algorithms, the trained ONNs are applied in the classification tasks for iris plants dataset, wine recognition dataset and modulation formats recognition. The calculated results exhibit that the training algorithms based on neuroevolution are competitive with other traditional learning algorithms on both accuracy and stability. Compared with previous works, we introduce an efficient training method for the ONNs and demonstrate their broad application prospects in pattern recognition, reinforcement learning and so on. IntroductionArtificial neural networks (ANNs), deep learning [1] in particular, has attracted a great deal of research attentions for an impressively large number of applications, such as image processing [2], natural language processing [3], acoustical signal processing [4], time series processing [5], self-driving [6], games [7], robot [8] and so on. It should be noted that the training of the ANNs with deep hidden layers, especially for convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for example AlexNet [9], VGGNet [10], GoogLeNet [11], ResNet [12] and long short-term memory [13], typically demands significant computational time and resources [1]. Thus, various electronic special-purpose platforms based on graphical processing units (GPUs) [14], field-programmable gate arrays (FPGAs) [15] and applicationspecific integrated circuits (ASICs) [16] were invented to accelerate the training and inference process of deep learning. On the other hand, in order to obtain general artificial intelligence, some brain-inspired chips including IBM TrueNorth [17], Intel Loihi [18], and SpiNNaker [19]were designed by imitating the structure of a brain. However, even both energy efficiency and speed were improved, the performances of the brain-inspired chips were difficult to compete with the state of the art of deep learning [20]. In the recent years, optical computing had been demonstrated as an effective alternative to traditionally electronic computing architectures and expected to alleviate the bandwidth bottlenecks and power consumption in electronics [21]. For example, new photonic approaches for spiking neuron and scalable network architecture based upon excitable lasers, broadcast-and-weight protocol and reservoir computing had been illustrated [22][23][24]. Despite ultrafast spiking response were achiev...

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Torus-connected network is widely used in modern supercomputers due to its linear per node cost scaling and its competitive overall performance. Job scheduling system plays a critical role for the efficient use of supercomputers. As supercomputers continue growing in size, a fundamental problem arises: how to effectively balance job performance with system performance on torus-connected machines? In this work, we will present a new scheduling design named window-based locality-aware scheduling. Our design contains three novel features. First, rather than oneby-one job scheduling, our design takes a "window" of jobs, i.e. multiple jobs, into consideration for job prioritizing and resource allocation. Second, our design maintains a list of slots to preserve node contiguity information for resource allocation. Finally, we formulate our scheduling decision making into a 0-1 Multiple Knapsack Problem and present two algorithms to solve the problem. A series of trace-based simulations using job logs collected from production supercomputers indicate that this new scheduling design has real potentials and can effectively balance job performance and system performance.