The energy efficiency of Artificial Intelligence (AI) systems is a crucial and actual issue that may have an important impact on an ecological, economic and technological level. Spiking Neural Networks (SNNs) are strongly suggested as valid candidates able to overcome Artificial Neural Networks (ANNs) in this specific contest. In this study, the proposal involves the review and comparison of energy consumption of the popular Artificial Neural Network architectures implemented on the CPU and GPU hardware compared with Spiking Neural Networks implemented in specialized memristive hardware and biological neural network human brain. As a result, the energy efficiency of Spiking Neural Networks can be indicated from 5 to 8 orders of magnitude. Some Spiking Neural Networks solutions are proposed including continuous feedback-driven self-learning approaches inspired by biological Spiking Neural Networks as well as pure memristive solutions for Spiking Neural Networks.

中文翻译：

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推进神经网络：创新和对能源消耗的影响


人工智能 （AI） 系统的能源效率是一个关键且实际的问题，可能对生态、经济和技术层面产生重要影响。强烈建议将 Spiking 神经网络 （SNN） 作为能够在此特定比赛中克服人工神经网络 （ANN） 的有效候选者。在这项研究中，该提案涉及审查和比较在 CPU 和 GPU 硬件上实现的流行的人工神经网络架构与在专用忆阻硬件和生物神经网络人脑中实现的脉冲神经网络的能耗。因此，脉冲神经网络的能效可以表示 5 到 8 个数量级。提出了一些脉冲神经网络解决方案，包括受生物脉冲神经网络启发的连续反馈驱动自学习方法，以及脉冲神经网络的纯忆阻解决方案。