Precise neural network computation with imprecise analog devices

Understand

The operations used for neural network computation map favorably onto simple analog circuits, which outshine their digital counterparts in terms of compactness and efficiency.

• Nevertheless, such implementations have been largely supplanted by digital designs, partly because of device mismatch effects due to material and fabrication imperfections.
• We propose a framework that exploits the power of deep learning to compensate for this mismatch by incorporating the measured device variations as constraints in the neural network training process.
• This eliminates the need for mismatch minimization strategies and allows circuit complexity and power-consumption to be reduced to a minimum.