Training data is usually increased to improve the performance of models. This increase in data lengthens training time and consumes more power. Current hardware improvements help lower training time, but this is not a sustainable approach to AI. It is projected that energy consumption in the EU will rise by 30% in 2026 due to AI and other related factors. The cost to train LLMs will continue to rise if the current trend continues, and this cost increase will be transferred to AI technology users. Neuromorphic computing can help solve this energy consumption crisis, reduce expenditure on AI, and promote more sustainable AI use to protect the planet. CTOs and IT leaders should understand how this technology works so they can limit their energy use, plan for the future, and stay ahead of competitors.
How Neuromorphic Computing Works
Neuromorphic computing resembles the human brain …