Fast and robust analog in-memory deep neural network training.

Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory computing to accelerate the inference phase has been studied extensively, accelerating the training phase has received less attention, despite its arguably much larger compute demand to accelerate. While some analog in-memory training algorithms have been suggested, they either invoke significant amount of auxiliary digital compute-accumulating the gradient in digital floating point precision, limiting the potential speed-up-or suffer from the need for near perfectly programming reference conductance values to establish an algorithmic zero point.