深部张量压缩的前后预报
Back-and-Forth prediction for deep tensor compression
摘要：
最近的人工智能应用，例如具有神经网络的协作智能，涉及在各种计算设备之间传输深度特征张量。这需要张量压缩，以便优化设备之间带宽受限通道的使用。在本文中，我们提出了一种称为前后（BaF）预测的预测方案，该方案是为深度特征张量开发的，它允许我们显着减小张量大小并提高其可压缩性。我们与最先进的对象检测器的实验表明，所提出的方法使我们能够显着减少压缩从模型中提取的特征张量所需的位数，检测性能的下降可以忽略不计，并且不需要对网络权重进行任何重新训练。我们实现了张量大小减少62%和75%，同时保持网络准确性的损失分别小于1%和2%。
英文摘要：
Recent AI applications such as Collaborative Intelligence with neural networks involve transferring deep feature tensors between various computing devices. This necessitates tensor compression in order to optimize the usage of bandwidth-constrained channels between devices. In this paper we present a prediction scheme called Back-and-Forth (BaF) prediction, developed for deep feature tensors, which allows us to dramatically reduce tensor size and improve its compressibility. Our experiments with a state-of-the-art object detector demonstrate that the proposed method allows us to significantly reduce the number of bits needed for compressing feature tensors extracted from deep within the model, with negligible degradation of the detection performance and without requiring any retraining of the network weights. We achieve a 62% and 75% reduction in tensor size while keeping the loss in accuracy of the network to less than 1% and 2%, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
