  In existing mobile network systems, the data plane (DP) is mainly considered
a pipeline consisting of network elements end-to-end forwarding user data
traffics. With the rapid maturity of programmable network devices, however,
mobile network infrastructure mutates towards a programmable computing
platform. Therefore, such a programmable DP can provide in-network computing
capability for many application services. In this paper, we target to enhance
the data plane with in-network deep learning (DL) capability. However,
in-network intelligence can be a significant load for network devices. Then,
the paradigm of the functional split is applied so that the deep neural network
(DNN) is decomposed into sub-elements of the data plane for making machine
learning inference jobs more efficient. As a proof-of-concept, we take a Blind
Source Separation (BSS) problem as an example to exhibit the benefits of such
an approach. We implement the proposed enhancement in a full-stack emulator and
we provide a quantitative evaluation with professional datasets. As an initial
trial, our study provides insightful guidelines for the design of the future
mobile network system, employing in-network intelligence (e.g., 6G).
