Neuromorphic electronic devices vs traditional computers: energy consumption and performance

What is it about?

Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. Neuromorphic engineering promises extremely low energy consumptions, comparable to those of the nervous system. However, until now the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, rendering elusive a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. These circuits emulate enough neurons to compete with state-of-the-art classifiers. We also show that the energy consumption of the IBM chip is typically 2 or more orders of magnitude lower than that of conventional digital machines when implementing classifiers with comparable performance.

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Why is it important?

We believe we finally have a fair comparison between the energy consumption of a neuromorphic chip and the energy consumption of more conventional digital devices. The results are interesting as the neuromorphic chip consumes at least 2 orders of magnitude less energy than conventional electronic devices. These are exciting news for the neuromorphic hardware community, as it is now possible to show that neuromorphic devices can be used in real world applications using only a tiny fraction of the energy used by conventional digital devices.