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Hardware neural networks that implement synaptic weights with embeddednon-volatile memory, such as spin torque memory (ST-MRAM), are a major lead forlow energy artificial intelligence. In this work, we propose an approximatestorage approach for their memory. We show that this strategy grants effectivecontrol of the bit error rate by modulating the programming pulse amplitude orduration. Accounting for the devices variability issue, we evaluate energysavings, and show how they translate when training a hardware neural network.On an image recognition example, 74% of programming energy can be saved bylosing only 1% on the recognition performance.

Use of Magnetoresistive Random-Access Memory as Approximate Memory for Training Neural Networks