神经形态算法在科学计算中的内在数值鲁棒性与容错性
Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computing
AI总结：
本文提出了一种神经形态算法，展示其在求解偏微分方程时对神经元和脉冲丢失的高鲁棒性，并通过结构超参数调节其容错能力。
AI中文摘要：
神经形态计算提供内在容错性的潜力长期以来被推测，但大脑在神经形态应用中的鲁棒性尚未得到验证。本文显示，一种之前描述的原生脉冲神经形态算法在求解偏微分方程时，对结构扰动形式的消融神经元和丢失脉冲具有内在容忍性。容忍带很大：我们发现，在精度结果显著下降之前，多达32%的神经元和高达90%的脉冲可能完全丢失。此外，这种鲁棒性可通过结构超参数进行调节。本工作证明了算法背后的特定大脑启发式方法对预期的大脑启发式神经形态算法的鲁棒性贡献显著。
英文摘要：
The potential for neuromorphic computing to provide intrinsic fault tolerance has long been speculated, but the brain's robustness in neuromorphic applications has yet to be demonstrated. Here, we show that a previously described, natively spiking neuromorphic algorithm for solving partial differential equations is intrinsically tolerant to structural perturbations in the form of ablated neurons and dropped spikes. The tolerance band for these perturbations is large: we find that as many as 32 percent of the neurons and up to 90 percent of the spikes may be entirely dropped before a significant degradation in the accuracy results. Furthermore, this robustness is tunable through structural hyperparameters. This work demonstrates that the specific brain-like inspiration behind the algorithm contributes to a significant degree of robustness expected from brain-like neuromorphic algorithms.
