Reversible computing can help reclaim your chip's wasted energy

The article discusses the significant energy inefficiency in modern AI hardware, where nearly all electrical energy consumed by processors is lost as heat due to fundamental limitations in conventional CMOS transistor technology. This inefficiency is especially critical as generative AI models like ChatGPT demand substantially more power per query compared to traditional searches, contributing to data centers potentially consuming up to 12% of US electricity by 2030. The root cause lies in abrupt transistor switching in CMOS chips, which dissipates energy as heat and imposes costly cooling requirements and scalability challenges. Vaire Computing, a startup based in the US and UK, proposes a solution through reversible computing using adiabatic switching. This approach gradually transfers electrical charge during transistor switching, significantly reducing energy loss by preserving and recycling information rather than erasing it, thereby circumventing Landauer’s principle that links information deletion to heat generation. Vaire’s prototypes currently reclaim about 50% of wasted computational energy, with expectations for even greater efficiency improvements. This innovation could mark a