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Deep Learning is ubiquitous in a wide field of applications ranging from research to industry. In comparison to time-consuming iterative training of convolutional neural networks (CNNs), inference is a relatively lightweight operation making it amenable to execution on mobile devices. Nevertheless, lower latency and higher computation efficiency are crucial to allow for complex models and prolonged battery life. Addressing the aforementioned challenges, we propose FeatherCNN – a fast inference library for ARM CPUs – targeting the performance ceiling of mobile devices. FeatherCNN employs three key techniques: 1) A highly efficient TensorGEMM (generalized matrix multiplication) routine is applied to accelerate Winograd convolution on ARM CPUs, 2) General layer optimization based on custom high performance kernels improves both the computational efficiency and locality of memory access patterns for non-Winograd layers. 3) The framework design emphasizes joint layer-wise optimization using layer fusion to remove redundant calculations and memory movements. Performance evaluation reveals that FeatherCNN significantly outperforms state-of-the-art libraries. A forward propagation pass of VGG-16 on a 64-core ARM server is 48, 14, and 12 times faster than Caffe using OpenBLAS, Caffe2 using Eigen, and NNPACK, respectively. In addition, FeatherCNN is 3.19 times faster than the recently released TensorFlow Lite library on an iPhone 7 plus. In terms of GEMM performance, FeatherCNN achieves 14.8 and 39.0 percent higher performance than Apple's Accelerate framework on an iPhone 7 plus and Eigen on a Samsung Galaxy S8, respectively. The source code of FeatherCNN library is publicly available at https://github.com/tencent/feathercnn .

This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on Honor of Kings, one of the world's most popular games at present. Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning. It is a multi-agent problem with one agent competing against its opponent; and it requires the generalization ability as it has diverse targets to control and diverse opponents to compete with. We describe the observation, action, and reward specifications for the Honor of Kings domain and provide an open-source Python-based interface for communicating with the game engine. We provide twenty target heroes with a variety of tasks in Honor of Kings Arena and present initial baseline results for RL-based methods with feasible computing resources. Finally, we showcase the generalization challenges imposed by Honor of Kings Arena and possible remedies to the challenges. All of the software, including the environment-class, are publicly available at https://github.com/tencent-ailab/hok_env . The documentation is available at https://aiarena.tencent.com/hok/doc/ ., Comment: Accepted by NeurIPS 2022