Lightweight compression of neural network feature tensors for collaborative intelligence

• URL: http://arxiv.org/abs/2105.06002v1
• Date: Wed, 12 May 2021 23:41:35 GMT
• Title: Lightweight compression of neural network feature tensors for collaborative intelligence
• Authors: Robert A. Cohen, Hyomin Choi, Ivan V. Baji\'c
• Abstract summary: 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. This paper presents a novel lightweight compression technique designed specifically to code the activations of a split DNN layer.
• Score: 32.03465747357384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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