论文标题
通过近似SoftMax和壁球操作使边缘的胶囊网络启用胶囊网络
Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations
论文作者
论文摘要
复杂的深层神经网络（例如胶囊网络（CAPSNETS））以计算密集型操作为代价表现出较高的学习能力。为了使其在边缘设备上的部署，我们建议利用近似计算来设计诸如SoftMax和Squash等复杂操作的近似变体。在我们的实验中，与确切功能相比，我们评估了通过ASIC设计流实施的设计和量化capsnets的准确性的区域，功耗和关键路径延迟之间的权衡。
Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit high learning capabilities at the cost of compute-intensive operations. To enable their deployment on edge devices, we propose to leverage approximate computing for designing approximate variants of the complex operations like softmax and squash. In our experiments, we evaluate tradeoffs between area, power consumption, and critical path delay of the designs implemented with the ASIC design flow, and the accuracy of the quantized CapsNets, compared to the exact functions.
