  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.
