论文标题
使用星形胶质细胞神经网络的耐故障计算的设计方法
A Design Methodology for Fault-Tolerant Computing using Astrocyte Neural Networks
论文作者
论文摘要
我们提出了一种设计方法，以促进深度学习模型的容错性。 First, we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and synapse circuitries in each neuromorphic core are enclosed with astrocyte circuitries, the star-shaped glial cells of the brain that facilitate self-repair by restoring the spike firing frequency of a failed neuron using a closed-loop retrograde feedback signal.接下来，我们在深度学习模型中介绍星形胶质细胞，以达到对硬件故障的必要程度。最后，我们使用系统软件将支持星形胶质细胞模型的模型划分为簇，并在建议的耐故障神经形态设计上实现它们。我们使用七个深度学习推论模型评估了这种设计方法，并表明它既是区域又是电力效率。
We propose a design methodology to facilitate fault tolerance of deep learning models. First, we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and synapse circuitries in each neuromorphic core are enclosed with astrocyte circuitries, the star-shaped glial cells of the brain that facilitate self-repair by restoring the spike firing frequency of a failed neuron using a closed-loop retrograde feedback signal. Next, we introduce astrocytes in a deep learning model to achieve the required degree of tolerance to hardware faults. Finally, we use a system software to partition the astrocyte-enabled model into clusters and implement them on the proposed fault-tolerant neuromorphic design. We evaluate this design methodology using seven deep learning inference models and show that it is both area and power efficient.
