Rethinking Pruning for Accelerating Deep Inference at the Edge

There is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses the size of the deep neural network without severe drop in inference accuracy. However, we observe that although existing network pruning algorithms prove effective to speed up the prior deep neural network, they lead to dramatic slowdown of the subsequent decoding and may not always reduce the overall latency of the entire application. To rectify such drawbacks, we propose entropy-based pruning, a new regularizer that can be seamlessly integrated into existing network pruning algorithms. Our key theoretical insight is that reducing the information entropy of the deep neural network outputs decreases the upper bound of the subsequent decoding search space. We validate our solution with two state-of-the-art network pruning algorithms on two model architectures. Experimental results show that compared with existing network pruning algorithms, our entropy-based pruning method notably suppresses and even eliminates the increase of decoding time, and achieves shorter overall latency with only negligible extra accuracy loss in the applications.