The spiking neural network (SNN) has been attracting considerable attention not only as a mathematical model for the brain, but also as an energy-efficient information processing model for real-world applications. In particular, SNNs based on temporal coding are expected to be much more efficient than those based on rate coding, because the former requires substantially fewer spikes to carry out tasks. As SNNs are continuous-state and continuous-time models, it is favorable to implement them with analog VLSI circuits. However, the construction of the entire system with continuous-time analog circuits would be infeasible when the system size is very large. Therefore, mixed-signal circuits must be employed, and the time discretization and quantization of the synaptic weights are necessary. Moreover, the analog VLSI implementation of SNNs exhibits non-idealities, such as the effects of noise and device mismatches, as well as other constraints arising from the analog circuit operation. In this study, we investigated the effects of the time discretization and/or weight quantization on the performance of SNNs. Furthermore, we elucidated the effects the lower bound of the membrane potentials and the temporal fluctuation of the firing threshold. Finally, we propose an optimal approach for the mapping of mathematical SNN models to analog circuits with discretized time.
翻译:神经神经网络(SNN)不仅作为大脑的数学模型,而且作为真实世界应用的节能信息处理模型,吸引了相当的注意力。特别是,基于时间编码的SNNN预计比基于速率编码的SNNN效率要高得多,因为前者需要的钉子要少得多才能执行任务。由于SNNN是连续状态和连续时间模型,因此使用模拟VLSI电路来实施这些模型是可取的。然而,在系统规模非常大时,用连续时间模拟电路建造整个系统是行不通的。因此,必须使用混合信号电路,并且基于时间编码的SNNNNN的时分分化和四分化比重是有必要的。此外,SLSI的模拟VLSI实施模拟显示非理想性,例如噪音和装置不匹配,以及模拟电路运行的其他限制。我们研究了时间分解和(或)重量四分解对SNNNN的运行效果的影响。最后,我们将SNNNN的时序模型的时序模型与最短的模拟方向图绘制结果捆绑起来。