  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.
