  In collaborative intelligence applications, part of a deep neural network
(DNN) is deployed on a lightweight device such as a mobile phone or edge
device, and the remaining portion 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 quantize and
compress the features output by the intermediate layer of a split DNN, without
requiring any retraining of the network weights. Mathematical models for
estimating the clipping and quantization error of ReLU and leaky-ReLU
activations at this intermediate layer are developed and used to compute
optimal clipping ranges for coarse quantization. We also present a modified
entropy-constrained design algorithm for quantizing clipped activations. When
applied to popular object-detection and classification DNNs, we were able to
compress the 32-bit floating point intermediate 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 an
intermediate layer of a split neural network for edge/cloud applications.
