2022 International Electron Devices Meeting (IEDM) 2022
DOI: 10.1109/iedm45625.2022.10019482
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Device Variation-Aware Adaptive Quantization for MRAM-based Accurate In-Memory Computing Without On-chip Training

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“…Nonvolatile random-access memory (RAM) technologies such as resistive RAM, phase change RAM, magnetoresistive RAM (MRAM), and ferroelectric RAM have been proven exceptionally suitable and successful in hardware-accelerated matrix multiplication, a burdensome operation predominant in AI training and computing, owing to their nonvolatility and crossbar structure. Nevertheless, matrix multiplication is just one side of the coin in stochastic neural networks.…”
mentioning
confidence: 99%
“…Nonvolatile random-access memory (RAM) technologies such as resistive RAM, phase change RAM, magnetoresistive RAM (MRAM), and ferroelectric RAM have been proven exceptionally suitable and successful in hardware-accelerated matrix multiplication, a burdensome operation predominant in AI training and computing, owing to their nonvolatility and crossbar structure. Nevertheless, matrix multiplication is just one side of the coin in stochastic neural networks.…”
mentioning
confidence: 99%