Multi-task deep learning network and generation method thereof

A multi-task deep learning network, suitable for an electronic apparatus, comprising:

• at least one shared layer, wherein the at least one shared layer is configured to receive a plurality of pieces of input information and generate a plurality of pieces of processed feature information;
• and
• a plurality of groups of task-specific layers, wherein the groups of task-specific layers compute and generate a plurality of groups of output information corresponding to a plurality of different tasks according to the pieces of processed feature information,
• wherein each one of the groups of task-specific layers is activated in stages according to a power supply state of the electronic apparatus and a power consumption requirement of each of the task-specific layers,
• wherein the groups of task-specific layers are generated by a processor with computing power, and the processor is configured to:
• creating a plurality of pieces of power consumption information corresponding to a plurality of arithmetic units;
• selecting plural ones among the arithmetic units to build a selected model based on minimum total power consumption according to a corresponding accuracy requirement and the pieces of power consumption information for each one among the groups of task-specific layers;
• performing a network weight sparsity analysis and a utilization evaluation to optimize network pruning to generate a hardware-aware model according to the selected model;
• and
• generating each one of the groups of task-specific layers according to the hardware-aware model.