Edge machine learning (ML) enables localized processing of data on devices
and is underpinned by deep neural networks (DNNs). However, DNNs cannot be
easily run on devices due to their substantial computing, memory and energy
requirements for delivering performance that is comparable to cloud-based ML.
Therefore, model compression techniques, such as pruning, have been considered.
Existing pruning methods are problematic for edge ML since they: (1) Create
compressed models that have limited runtime performance benefits (using
unstructured pruning) or compromise the final model accuracy (using structured
pruning), and (2) Require substantial compute resources and time for
identifying a suitable compressed DNN model (using neural architecture search).
In this paper, we explore a new avenue, referred to as
Pruning-at-Initialization (PaI), using structured pruning to mitigate the above
problems. We develop Reconvene, a system for rapidly generating pruned models
suited for edge deployments using structured PaI. Reconvene systematically
identifies and prunes DNN convolution layers that are least sensitive to
structured pruning. Reconvene rapidly creates pruned DNNs within seconds that
are up to 16.21x smaller and 2x faster while maintaining the same accuracy as
an unstructured PaI counterpart.

利用结构化剪枝技术，Reconvene 系统可快速生成适用于边缘部署的经剪枝模型，大小减小了 16.21 倍，速度加快 2 倍，同时保持与未经剪枝的模型相同的准确性。