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We assess the impact of the conductance response of Non-Volatile Memory (NVM) devices employed as the synaptic weight element for on-chip acceleration of the training of large-scale artificial neural networks (ANN). We briefly review our previous work towards achieving competitive performance (classification accuracies) for such ANN with both Phase-Change Memory (PCM) [1], [2] and non-filamentary ReRAM based on PrCaMnO (PCMO) [3], and towards assessing the potential advantages for ML training over GPU–based hardware in terms of speed (up to 25x faster) and power (from 120–2850x lower power) [4]. We then discuss the “jump-table” concept, previously introduced to model real-world NVM such as PCM [1] or PCMO, to describe the full cumulative distribution function (CDF) of conductance-change at each device conductance value, for both potentiation (SET) and depression (RESET). Using several types of artificially–constructed jump-tables, we assess the relative importance of deviations from an ideal NVM with perfectly linear conductance response.
Anastasia Ailamaki, Viktor Sanca, Hamish Mcniece Hill Nicholson, Andreea Nica, Syed Mohammad Aunn Raza
Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson, Syed Mohammad Aunn Raza