As deep learning models scale, they become increasingly competitive from domains spanning 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 the one of 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 SNNs needs further investigation, potentially taking inspiration from the available work done on the artificial neural networks (ANN s) 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 ANN s and SNNs.

翻译：随着深度学习模型规模不断扩大，它们在计算机视觉到自然语言处理等多个领域展现出越来越强的竞争力；然而，这是以牺牲效率为代价的，因为这类模型需要更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模型；因此，神经形态计算试图模仿大脑的运作方式，例如基于脉冲的信息处理，以提高深度学习模型的效率。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，但现有的生物基质严重限制了生物大脑的进化。电子硬件则不存在相同的限制；因此，尽管对脉冲神经网络进行建模可能揭示问题的部分答案，但针对SNN的高效硬件后端设计仍需进一步研究，并可能借鉴人工神经网络领域已有的研究成果。那么，在设计新硬件时，何时应当借鉴大脑，何时又应忽略它？为回答这一问题，我们定量比较了ANN和SNN的数字硬件加速技术与平台。