This paper investigates the energy savings that near-subthreshold processors
can obtain in edge AI applications and proposes strategies to improve them
while maintaining the accuracy of the application. The selected processors
deploy adaptive voltage scaling techniques in which the frequency and voltage
levels of the processor core are determined at the run-time. In these systems,
embedded RAM and flash memory size is typically limited to less than 1 megabyte
to save power. This limited memory imposes restrictions on the complexity of
the neural networks model that can be mapped to these devices and the required
trade-offs between accuracy and battery life. To address these issues, we
propose and evaluate alternative 'big-little' neural network strategies to
improve battery life while maintaining prediction accuracy. The strategies are
applied to a human activity recognition application selected as a demonstrator
that shows that compared to the original network, the best configurations
obtain an energy reduction measured at 80% while maintaining the original level
of inference accuracy.

该论文研究了在边缘人工智能应用中实现接近亚阈值处理器的能源节约，并提出了改进策略，同时保持了应用的准确性。通过采用一些新的神经网络策略，作者成功地实现了在能效比与准确性之间的平衡。