In her roadmap, Georgia Tech professor Jennifer Hasler emphasizes analog processing’s key role in neuromorphic systems, specifically field programmable analog arrays. She claims Georgia Tech’s FGAAs “award the programmability and capability of the Anadigm components” by housing “hundreds of thousands of programmable parameters, enabling them to be used for system level computing, not just analog glue logic.“
Hasler believes the path to desktop neuromorphic systems will require analog system-on-chip approaches to achieve the low power devices necessary to emulate billions of brain-like neurons connected by trillions of learning synapses. She predicts that desktop neuromorphic systems that rival the compactness of the human brain will require a 100 million times reduction in power over the digital supercomputers simulating them today.