Convolutional neural networks (CNNs) require significant computing power during inference. Smart phones, for example, may not run a facial recognition system or search algorithm smoothly due to the lack of resources and supporting hardware. Methods for reducing memory size and increasing execution speed have been explored, but choosing effective techniques for an application requires extensive knowledge of the network architecture. This paper proposes a general approach to preparing a compressed deep neural network processor for inference with minimal additions to existing microprocessor hardware. To show the benefits to the proposed approach, an example CNN for synthetic aperture radar target classification is modified and complimentary custom processor instructions are designed. The modified CNN is examined to show the effects of the modifications and the custom processor instructions are profiled to illustrate the potential performance increase from the new extended instructions.

中文翻译：

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用于卷积神经网络的可扩展嵌入式处理器

卷积神经网络（CNN）在推理过程中需要大量的计算能力。例如，由于缺少资源和支持的硬件，智能手机可能无法流畅地运行面部识别系统或搜索算法。已经探索了减小存储器大小和提高执行速度的方法，但是为应用选择有效的技术需要对网络体系结构有广泛的了解。本文提出了一种通用的方法来准备压缩的深度神经网络处理器，以便在对现有微处理器硬件进行最少添加的情况下进行推理。为了展示所提出方法的好处，对用于合成孔径雷达目标分类的示例CNN进行了修改，并设计了互补的定制处理器指令。