论文标题
神经形态硬件系统的astromorigic自我修复
Astromorphic Self-Repair of Neuromorphic Hardware Systems
论文作者
论文摘要
尽管基于峰值神经网络（SNN）的神经形态计算体系结构越来越引起人们的兴趣，作为通往生物易变的机器学习的途径，但注意力仍然集中在诸如神经元和突触之类的计算单元上。本文从这种神经突触的角度转移，试图探索神经胶质细胞的自我修复作用，尤其是星形胶质细胞。该工作研究了与星形胶质细胞计算神经科学模型的更强相关性，以开发具有更高程度的生物效果的宏观建模，从而准确捕获了自我修复过程的动态行为。硬件软件共同设计分析表明，生物形态的星形胶质细胞调节有可能在神经形态硬件系统中自我修复硬件实际故障，具有更好的精度和维修收敛，以实现MNIST和F-MNIST数据集的无用学习任务。我们的实施源代码和训练有素的模型可在https://github.com/neurocomplab-psu/astromorphic_sers_repair上获得。
While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse. Shifting from this neuro-synaptic perspective, this paper attempts to explore the self-repair role of glial cells, in particular, astrocytes. The work investigates stronger correlations with astrocyte computational neuroscience models to develop macro-models with a higher degree of bio-fidelity that accurately captures the dynamic behavior of the self-repair process. Hardware-software co-design analysis reveals that bio-morphic astrocytic regulation has the potential to self-repair hardware realistic faults in neuromorphic hardware systems with significantly better accuracy and repair convergence for unsupervised learning tasks on the MNIST and F-MNIST datasets. Our implementation source code and trained models are available at https://github.com/NeuroCompLab-psu/Astromorphic_Self_Repair.
