Boosting the task accuracy of tiny neural networks (TNNs) has become a
fundamental challenge for enabling the deployments of TNNs on edge devices
which are constrained by strict limitations in terms of memory, computation,
bandwidth, and power supply. To this end, we propose a framework called
NetDistiller to boost the achievable accuracy of TNNs by treating them as
sub-networks of a weight-sharing teacher constructed by expanding the number of
channels of the TNN. Specifically, the target TNN model is jointly trained with
the weight-sharing teacher model via (1) gradient surgery to tackle the
gradient conflicts between them and (2) uncertainty-aware distillation to
mitigate the overfitting of the teacher model. Extensive experiments across
diverse tasks validate NetDistiller's effectiveness in boosting TNNs'
achievable accuracy over state-of-the-art methods. Our code is available at
this https URL

通过将微小神经网络（TNNs）作为权重共享教师网络的子网络，利用梯度手术和不确定性感知蒸馏两种方法，我们提出了 NetDistiller 框架，以提高 TNNs 的可实现准确性，从而解决边缘设备的内存、计算、带宽和电源供应的限制问题。实验证实了 NetDistiller 在提升 TNNs 可实现准确性方面的有效性，胜过其他最先进的方法。