The rapid growth of microcontroller-based IoT devices has opened up numerous applications, from smart manufacturing to personalized healthcare. Despite the widespread adoption of energy-efficient microcontroller units (MCUs) in the Tiny Machine Learning (TinyML) domain, they still face significant limitations in terms of performance and memory (RAM, Flash). In this work, we combine approximate computing and software kernel design to accelerate the inference of approximate CNN models on MCUs. Our kernel-based approximation framework firstly unpacks the operands of each convolution layer and then conducts an offline calculation to determine the significance of each operand. Subsequently, through a design space exploration, it employs a computation skipping approximation strategy based on the calculated significance. Our evaluation on an STM32-Nucleo board and 2 popular CNNs trained on the CIFAR-10 dataset shows that, compared to state-of-the-art exact inference, our Pareto optimal solutions can feature on average 21% latency reduction with no degradation in Top-1 classification accuracy, while for lower accuracy requirements, the corresponding reduction becomes even more pronounced.

本研究解决了Tiny Machine Learning（TinyML）在微控制器中推理效率和内存性能的不足问题。我们提出了一种结合近似计算和软件内核设计的新方法，通过对卷积层操作数进行解包和意义计算，采用计算跳过策略进行推理加速。实验结果表明，这种方法在保持分类准确性的同时，平均减少了21%的延迟，对于低准确率需求，则能实现更显著的优化。

通过近似内核加速微控制器上的TinyML推理