Inference of long-short term memory networks at software-equivalent accuracy using 2.5 M analog phase change memory devices
2019 Symposium on VLSI Technology, 2019•ieeexplore.ieee.org
We report accuracy for forward inference of long-short-term-memory (LSTM) networks using
weights programmed into the conductances of phase-change memory (PCM) devices. We
demonstrate strategies for software weight-mapping and programming of hardware analog
conductances that provide accurate weight programming despite significant device
variability. Inference accuracy very close to software-model baselines is achieved on several
language modeling tasks.
weights programmed into the conductances of phase-change memory (PCM) devices. We
demonstrate strategies for software weight-mapping and programming of hardware analog
conductances that provide accurate weight programming despite significant device
variability. Inference accuracy very close to software-model baselines is achieved on several
language modeling tasks.
We report accuracy for forward inference of long-short-term-memory (LSTM) networks using weights programmed into the conductances of phase-change memory (PCM) devices. We demonstrate strategies for software weight-mapping and programming of hardware analog conductances that provide accurate weight programming despite significant device variability. Inference accuracy very close to software-model baselines is achieved on several language modeling tasks.
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