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.

该研究比较了人工神经网络和脉冲神经网络的数字硬件加速技术和平台，以回答在设计新硬件时何时应参考大脑并何时应忽略的问题。