Embracing the unreliability of memory devices for neuromorphic computing
Résumé
The emergence of resistive non-volatile memories opens the way to highly energy-efficient computation near-or in-memory. However, this type of computation is not compatible with conventional ECC, and has to deal with device unreliability. Inspired by the architecture of animal brains, we present a manufactured differential hybrid CMOS/RRAM memory architecture suitable for neural network implementation that functions without formal ECC. We also show that using low-energy but error-prone programming conditions only slightly reduces network accuracy.
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Bocqet, IRPS2020, Embracing the Unreliability of Memory Devices for Neuromorphic Computing.pdf (1.59 Mo)
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