The computational workload involved in CNNs is typically out of reach for low-power embedded devices. The Approximate Computing paradigm can be exploited to reduce the CNN complexity since it improves performances and energy-efficiency by relaxing the need for fully accurate operations. In this work, we target weight-sharing as an approximate technique to reduce the memory footprint of a CNN. More in detail, we prove that optimizing the number of shared weights can enable significant network memory compression without noticeable accuracy loss without retraining or fine-tuning steps. However, we observe that the exploration time can easily explode in state-of-the-art CNNs. We thus propose the use of a fast accuracy estimation metric to guide the design space exploration and drastically reduce the exploration time up to 12×. Compared with state-of-the-art CNN approximation methods, we obtained more than 4× compression on GoogleNet on the ImageNet dataset with less than 1% accuracy loss in less than 5 h and without any retraining step.

CNN涉及的计算工作量通常对于低功耗嵌入式设备而言是遥不可及的。可以利用“近似计算”范式来减少CNN复杂度，因为它可以通过放宽对完全精确操作的需求来提高性能和能效。在这项工作中，我们将权重共享作为一种近似技术来减少CNN的内存占用。更详细地说，我们证明优化共享权重的数量可以实现显着的网络内存压缩，而不会造成明显的精度损失，而无需重新训练或微调步骤。但是，我们观察到，在最先进的CNN中，勘探时间很容易爆炸。因此，我们建议使用快速准确度估算指标来指导设计空间探索，并将探索时间大幅缩短至12倍。