Generalizing Reuse Patterns for Efficient DNN on Microcontrollers
Jiesong Liu, Bin Ren, Xipeng Shen · 2025
Deep Neural Networks (DNNs) face challenges in deployment on resource-constrained devices due to their high computational demands. Leveraging redundancy in input data and activation maps for computation reuse is an effective way to accelerate DNN inference, especially for microcontrollers where the computing power is very limited. This work points out an important limitation in current reuse-based DNN optimizations, the narrow definition of reuse patterns in data. It proposes the concept of generalized reuse and uncovers the relations between generalized reuse patterns and row/column reorder of a matrix view of the input or activation map of a DNN. It revolutionizes the conventional view of explorable reuse patterns, drastically expanding the reuse space. It further develops two novel analytical models for analyzing the impacts of reuse patterns on the accuracy and latency of DNNs, enabling efficient selection of appropriate reuse patterns. Experiments show that generalized reuse consistently brings significant benefits, regardless of the differences among DNNs or microcontroller hardware. It delivers 1.03-2.2x speedups or 1-8% accuracy improvement over conventional reuse.