始终个性化：使用提前退出实现高效的设备上CNN个性化
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
摘要：
由于强大的硬件和模型压缩技术的可用性，设备上的机器学习正在成为现实。通常，这些模型在大型GPU集群上进行预训练，并具有足够的参数来概括各种输入。在这项工作中，我们观察到可以采用更小的个性化模型来适应特定的场景，从而提高准确性和执行速度。然而，设备上的培训极具挑战性，即使对于旗舰智能手机也会带来过多的计算和内存需求。与此同时，设备上的数据可用性可能有限，样本通常没有标签。为此，我们引入了一个框架，它将早期出口附加到模型上，并在设备上对其进行个性化。这使得模型能够逐步绕过更大部分的计算，因为更多的个性化数据变得可用。此外，我们引入了一种有效的设备上算法，该算法以半监督的方式在整个网络的个性化时间的一小部分内训练早期退出。结果表明，EQUEPhonEE将准确率提高了15.9%，同时将训练成本降低了2.2倍，对于相同的准确率，平均推理延迟降低了2.2- 3.2倍，具体取决于设备上标签的可用性。
英文摘要：
On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques. Typically, these models are pretrained on large GPU clusters and have enough parameters to generalise across a wide variety of inputs. In this work, we observe that a much smaller, personalised model can be employed to fit a specific scenario, resulting in both higher accuracy and faster execution. Nevertheless, on-device training is extremely challenging, imposing excessive computational and memory requirements even for flagship smartphones. At the same time, on-device data availability might be limited and samples are most frequently unlabelled. To this end, we introduce PersEPhonEE, a framework that attaches early exits on the model and personalises them on-device. These allow the model to progressively bypass a larger part of the computation as more personalised data become available. Moreover, we introduce an efficient on-device algorithm that trains the early exits in a semi-supervised manner at a fraction of the whole network's personalisation time. Results show that PersEPhonEE boosts accuracy by up to 15.9% while dropping the training cost by up to 2.2x and inference latency by 2.2-3.2x on average for the same accuracy, depending on the availability of labels on-device.
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