Rain Neuromorphics has trained a deep learning network on an analog chip—a crossbar array of memristors—using the company’s analog-friendly training algorithms.
The process required many orders of magnitude less energy compared with today’s GPU systems. While Rain’s initial work has proven AI can be trained efficiently using analog chips, commercial realizations of the technology may still be a few years away.
In a paper co-authored with memristor pioneer Stanley Williams, Rain describes training single- and two-layer neural networks to recognize words written in braille. The setup uses a combination of two 64 x 64 memristor crossbar arrays (in this case, not the 3D ReRAM-based chip the company previously showed), combined with training algorithms using a technique called activity difference, which includes Rain’s earlier work on equilibrium propagation. Rain calls this hardware-algorithm combination memristor activity-difference energy minimization (MADEM).
https://www.eetimes.com/rain-demonstrates-ai-training-on-analog-chip/
Digitaalisten tekoälypiirien lisäksi on mahdollista toteuttaa tekoäly myös analogisilla piireillä.
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