System and method for training artificial neural networks

A method, comprising:

• performing, at a computing device that includes one or more processors, a first random access memory (RAM) comprising magnetic random access memory (MRAM), a second random access memory of a type distinct from MRAM, and a non-transitory computer-readable storage medium storing instructions for execution by the one or more processors, a set of operations that includes:
• receiving first data on which to train an artificial neural network (ANN);
• training the ANN by:
• using the first RAM comprising the MRAM and operating the first RAM as an error-prone memory by operating the MRAM using a given read pulse duration or a given write pulse duration and without performing error correction to realize a first bit-error rate as a function of the read pulse duration or the write pulse duration, the first bit-error rate being greater than a bit-error rate of the second RAM, performing a first set of training iterations to train the ANN using only the first data and storing a set of weights and a set of biases of the ANN in the first RAM;
• and
• after performing the first set of training iterations, using the second RAM of the type distinct from MRAM, performing a second set of training iterations to train the ANN using only the same first data and storing a set of weights and/or biases and a set of activations of the ANN in the second RAM;
• and
• storing values for the trained ANN, wherein the trained ANN is configured to classify untrained, unclassified second data based on the stored values, the untrained, unclassified second data being distinct from the first data.