In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a relatively low-complexity device such as a mobile phone or edge device, and the remainder of the DNN is processed where more computing resources are available, such as in the cloud. This paper presents a novel lightweight compression technique designed specifically to code the activations of a split DNN layer, while having a low complexity suitable for edge devices and not requiring any retraining. We also present a modified entropy-constrained quantizer design algorithm optimized for clipped activations. When applied to popular object-detection and classification DNNs, we were able to compress the 32-bit floating point activations down to 0.6 to 0.8 bits, while keeping the loss in accuracy to less than 1%. When compared to HEVC, we found that the lightweight codec consistently provided better inference accuracy, by up to 1.3%. The performance and simplicity of this lightweight compression technique makes it an attractive option for coding a layer's activations in split neural networks for edge/cloud applications.

本研究介绍了一种轻量级的压缩技术，用于在边缘设备上进行代码的分割，仅针对深度神经网络中的激活，而且不需要任何重新训练。当应用于流行的对象检测和分类深度神经网络时，能够将32位浮点激活压缩到0.6至0.8位，同时保持精度损失不到1％。

协作智能的神经网络特征张量轻量化压缩