As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense of efficiency since they require increasingly more memory and computing power. The power efficiency of the biological brain outperforms any large-scale deep learning ( DL ) model; thus, neuromorphic computing tries to mimic the brain operations, such as spike-based information processing, to improve the efficiency of DL models. Despite the benefits of the brain, such as efficient information transmission, dense neuronal interconnects, and the co-location of computation and memory, the available biological substrate has severely constrained the evolution of biological brains. Electronic hardware does not have the same constraints; therefore, while modeling spiking neural networks ( SNNs) might uncover one piece of the puzzle, the design of efficient hardware backends for SNN s needs further investigation, potentially taking inspiration from the available work done on the artificial neural networks ( ANNs) side. As such, when is it wise to look at the brain while designing new hardware, and when should it be ignored? To answer this question, we quantitatively compare the digital hardware acceleration techniques and platforms of ANNs and SNN s. As a result, we provide the following insights: (i) ANNs currently process static data more efficiently, (ii) applications targeting data produced by neuromorphic sensors, such as event-based cameras and silicon cochleas, need more investigation since the behavior of these sensors might naturally fit the SNN paradigm, and (iii) hybrid approaches combining SNN s and ANNs might lead to the best solutions and should be investigated further at the hardware level, accounting for both efficiency and loss optimization.

翻译：随着深度学习模型的扩展，它们在从计算机视觉到自然语言处理等领域的竞争力日益增强；然而，这以牺牲效率为代价，因为它们需要越来越多的内存和计算能力。生物大脑的能效优于任何大规模深度学习（DL）模型；因此，神经形态计算试图模拟大脑的操作，例如基于脉冲的信息处理，以提高DL模型的效率。尽管大脑具有高效信息传输、密集神经元互连以及计算与内存共置等优势，但可用的生物基质严重限制了生物大脑的进化。电子硬件没有相同的限制；因此，虽然对脉冲神经网络（SNN）进行建模可能揭示问题的某一方面，但为SNN设计高效的硬件后端需要进一步研究，可能从人工神经网络（ANN）方面的现有工作中汲取灵感。因此，在设计新硬件时，何时应该借鉴大脑，何时应忽略它？为回答这个问题，我们定量比较了ANN和SNN的数字硬件加速技术和平台。结果，我们提供了以下见解：（i）ANN目前能更高效地处理静态数据；（ii）针对神经形态传感器（如事件相机和硅耳蜗）产生的数据的应用需要更多研究，因为这些传感器的行为可能自然契合SNN范式；（iii）结合SNN和ANN的混合方法可能带来最佳解决方案，应在硬件层面进一步研究，同时考虑效率和损失优化。