  Currently, it is a hot research topic to realize accurate, efficient, and
real-time identification of massive spectral data with the help of deep
learning and IoT technology. Deep neural networks played a key role in spectral
analysis. However, the inference of deeper models is performed in a static
manner, and cannot be adjusted according to the device. Not all samples need to
allocate all computation to reach confident prediction, which hinders
maximizing the overall performance. To address the above issues, we propose a
Spectral data Classification framework with Adaptive Inference. Specifically,
to allocate different computations for different samples while better
exploiting the collaboration among different devices, we leverage Early-exit
architecture, place intermediate classifiers at different depths of the
architecture, and the model outputs the results when the prediction confidence
reaches a preset threshold. We propose a training paradigm of self-distillation
learning, the deepest classifier performs soft supervision on the shallow ones
to maximize their performance and training speed. At the same time, to mitigate
the vulnerability of performance to the location and number settings of
intermediate classifiers in the Early-exit paradigm, we propose a
Position-Adaptive residual network. It can adjust the number of layers in each
block at different curve positions, so it can focus on important positions of
the curve (e.g.: Raman peak), and accurately allocate the appropriate
computational budget based on task performance and computing resources. To the
best of our knowledge, this paper is the first attempt to conduct optimization
by adaptive inference for spectral detection under the IoT platform. We
conducted many experiments, the experimental results show that our proposed
method can achieve higher performance with less computational budget than
existing methods.
