Rain Neuromorphics Tapes Out Demo Chip for Analog AI (EET Asia)

Rain Neuromorphics Tapes Out Demo Chip for Analog AI

UF startup and UF Innovate | Accelerate graduate Rain Neuromorphics, an AI company which incubated at The Hub, has taped out a demonstration chip for its brain-inspired analog architecture that employs a 3D array of randomly-connected memristors to compute neural network training and inference at extremely low power.

Switching to entirely analog hardware for AI computation could allow a massive reduction in the power consumed by AI workloads. While some commercial chips currently use analog processor-in-memory techniques, they require digital conversion between network layers, consuming significant power. The limitations of current analog devices also means they can’t be used for training AI models since they are incompatible with back-propagation, the algorithm widely used for AI training. Rain’s aim is to build a complete analog chip, solving these issues with a combination of new hardware and a new training algorithm.

The company has changed direction on hardware over the last year. Analog computing chips use arrays of memristive elements, with commercial chips using memory cells such as flash. Rain previously used randomly deposited resistive nanowires, but has opted for resistive RAM (ReRAM) as the memristive element combined with 3D manufacturing techniques borrowed from NAND flash processes. The resulting configuration is based entirely on lithography.

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