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Java is very powerful, but in Deep Learning field, its capabilities probablyhas not been sufficiently exploited. Compared to the Java-baseddeep-learning-frameworks, the Python-based (PyTorch, TensorFlow, etc) areundoubtedly the mainstream, due to their easy-to-use, flexibility and betterecosystem. Dragon-Alpha is a Java-based Tensor Computing Framework, witheasy-to-use, high-scalability and high-performance, trying to break Java'sdilemma in deep learning field and make it more effective. Dragon-Alphasupports different levels of APIs, and can be used as a deep-learning-frameworkthrough its user-friendly high-level APIs. Dragon-Alpha has potential toaggregate computing-power across heterogeneous platforms and devices, based onits multi-layer architecture and Java's big-data ecosystem. Dragon-Alpha hasits asynchronized APIs to improve parallelism, and highly-optimized CUDAlibrary cu32 which adopts unique convolution\deconvolution operators for smallfeature maps. The experiments show that, compared to PyTorch&cuDNN,Dragon-Alpha&cu32 costs less time and memory (75.38% to 97.32%, 29.2% to66.4%), to train some typical neural networks (AlexNet, VGG, GoogleNet, ResNet)on Cifar-10.

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library