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 vegetable glycerin for sale clipping and quantization error of leaky-ReLU and ReLU activations at this intermediate layer are used to compute optimal clipping ranges for coarse quantization.A mathematical model for estimating the clipping and quantization error of leaky-ReLU activations puffy spa headband at this intermediate layer is 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.