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Deep neural networks have shown incredible performance for inference tasks ina variety of domains. Unfortunately, most current deep networks are enormouscloud-based structures that require significant storage space, which limitsscaling of deep learning as a service (DLaaS) and use for on-deviceintelligence. This paper is concerned with finding universal losslesscompressed representations of deep feedforward networks with synaptic weightsdrawn from discrete sets, and directly performing inference without fulldecompression. The basic insight that allows less rate than naive approaches isrecognizing that the bipartite graph layers of feedforward networks have a kindof permutation invariance to the labeling of nodes, in terms of inferentialoperation. We provide efficient algorithms to dissipate this irrelevantuncertainty and then use arithmetic coding to nearly achieve the entropy boundin a universal manner. We also provide experimental results of our approach onseveral standard datasets.

Universal and Succinct Source Coding of Deep Neural Networks