Advancing Neural Networks: Innovations and Impacts on Energy Consumption - Alina Fedorova; Nikola Jovišić; Jordi Vallverdù; Silvia Battistoni; Miloš Jovičić; Milovan Medojević; Alexander Toschev; Evgeniia Alshanskaia; Max Talanov; Victor Erokhin (Advanced Electronic Materials)
Abstract 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.