Neural network pruning is a key technique for deploying artificial intelligence (AI) models based on deep neural networks (DNNs) on resource-constrained platforms, such as mobile devices. However, hardware conditions and resource availability vary greatly across different platforms, making it essential to design pruned models optimally suited to specific hardware configurations.

Hardware-aware neural network pruning offers an effective way to automate this process, but it requires balancing multiple conflicting objectives, such as network accuracy, inference latency, and memory usage, that traditional mathematical methods struggle to solve.

In a study published in the journal Fundamental Research, a group of researchers from Shenzhen, China, present a novel hardware-aware neural network pruning approach based on multi-objective evolutionary optimization.

"We propose to employ Multi-Objective Evolutionary Algorithms (MOEAs) to solve the hardware neural network pruning problem," says Ke Tang, senior and corresponding author of the study.

Compared with conventional optimization algorithms, MOEAs have two advantages in tackling this problem. One is that MOEAs do not require particular assumptions like differentiability or continuity, and possess strong capacity for black-box optimization. The other is their ability to find multiple Pareto-optimal solutions in a single simulation run, which is very useful in practice because it offers flexibility to meet different user requirements.

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