  Deep learning has celebrated resounding successes in many application areas
of relevance to the Internet of Things (IoT), such as computer vision and
machine listening. These technologies must ultimately be brought directly to
the edge to fully harness the power of deep learning for the IoT. The obvious
challenge is that deep learning techniques can only be implemented on strictly
resource-constrained edge devices if the models are radically downsized. This
task relies on different model compression techniques, such as network pruning,
quantization, and the recent advancement of XNOR-Net. This study examines the
suitability of these techniques for audio classification on microcontrollers.
We present an application of XNOR-Net for end-to-end raw audio classification
and a comprehensive empirical study comparing this approach with
pruning-and-quantization methods. We show that raw audio classification with
XNOR yields comparable performance to regular full precision networks for small
numbers of classes while reducing memory requirements 32-fold and computation
requirements 58-fold. However, as the number of classes increases
significantly, performance degrades, and pruning-and-quantization based
compression techniques take over as the preferred technique being able to
satisfy the same space constraints but requiring approximately 8x more
computation. We show that these insights are consistent between raw audio
classification and image classification using standard benchmark sets. To the
best of our knowledge, this is the first study to apply XNOR to end-to-end
audio classification and evaluate it in the context of alternative techniques.
All codes are publicly available on GitHub.
