论文标题
硬件校准学习以补偿基于模拟RRAM的尖峰神经网络中的异质性
Hardware calibrated learning to compensate heterogeneity in analog RRAM-based Spiking Neural Networks
论文作者
论文摘要
尖峰神经网络（SNN）可以释放基于模拟的电阻随机访问记忆（RRAM）的全部功率，以用于低功率信号处理。它们固有的计算稀疏自然会带来能效的好处。实施强大SNN的主要挑战是模拟CMOS电路和RRAM技术的内在可变性（异质性）。在这项工作中，我们评估了使用130 \，NM技术节点设计和制造的基于RRAM的神经形态电路的性能和可变性。基于这些结果，我们提出了校准的神经形态硬件（NHC），其中学习电路在测量数据上进行了校准。我们表明，通过考虑到片外学习阶段的测得的异质性特征，NHC SNN自我校正其硬件非思想率，并学会了以很高的精度解决基准任务。这项工作演示了如何应对神经元和突触的异质性，以提高时间任务中的分类精度。
Spiking Neural Networks (SNNs) can unleash the full power of analog Resistive Random Access Memories (RRAMs) based circuits for low power signal processing. Their inherent computational sparsity naturally results in energy efficiency benefits. The main challenge implementing robust SNNs is the intrinsic variability (heterogeneity) of both analog CMOS circuits and RRAM technology. In this work, we assessed the performance and variability of RRAM-based neuromorphic circuits that were designed and fabricated using a 130\,nm technology node. Based on these results, we propose a Neuromorphic Hardware Calibrated (NHC) SNN, where the learning circuits are calibrated on the measured data. We show that by taking into account the measured heterogeneity characteristics in the off-chip learning phase, the NHC SNN self-corrects its hardware non-idealities and learns to solve benchmark tasks with high accuracy. This work demonstrates how to cope with the heterogeneity of neurons and synapses for increasing classification accuracy in temporal tasks.
