Could analog artificial intelligence (AI) hardware – rather than digital – tap fast, low-energy processing to solve machine learning’s rising costs and carbon footprint? Researchers say yes: Logan Wright and Tatsuhiro Onodera, research scientists at NTT Research and Cornell University, envision a future where machine learning (ML) will be performed with novel physical hardware, such as those based on photonics or nanomechanics. These unconventional devices, they say, could be applied in both edge and server settings. How analog AI hardware may one day reduce costs and carbon emissions | VentureBeat