  Recent studies have shown that collaborative intelligence (CI) is a promising
framework for deployment of Artificial Intelligence (AI)-based services on
mobile devices. In CI, a deep neural network is split between the mobile device
and the cloud. Deep features obtained at the mobile are compressed and
transferred to the cloud to complete the inference. So far, the methods in the
literature focused on transferring a single deep feature tensor from the mobile
to the cloud. Such methods are not applicable to some recent, high-performance
networks with multiple branches and skip connections. In this paper, we propose
the first bit allocation method for multi-stream, multi-task CI. We first
establish a model for the joint distortion of the multiple tasks as a function
of the bit rates assigned to different deep feature tensors. Then, using the
proposed model, we solve the rate-distortion optimization problem under a total
rate constraint to obtain the best rate allocation among the tensors to be
transferred. Experimental results illustrate the efficacy of the proposed
scheme compared to several alternative bit allocation methods.
