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- W2999143107 abstract "To improve energy efficiency and training time as compared to off-chip memory, pseudo crossbar array architecture with synaptic devices are gaining attraction for the on-chip realization of neuro-inspired learning algorithms. With recent advances in DNN training and inference acceleration using analog non-volatile memories, peripheral circuit design strategies are being explored to minimize the loss of accuracy and reduce read and write latency. In this paper, we use a time-based ADC (TADC) for read operation, which is area-effective compared to the Flash-ADC and time-efficient compared to the spike-counter based sense amplifiers. In the case of asymmetric conductance characteristics in multi-bit synaptic weight storage devices, the peripheral circuit design and read-write architecture become more compounded. In this paper, we propose an automated read-modify-write (RMW) scheme for multi-bit weight update in deep neural networks (DNNs) to improve the training accuracy with minimum average read-latency-demonstrated using a Ferroelectric-FET pseudo-crossbar array." @default.
- W2999143107 created "2020-01-23" @default.
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- W2999143107 date "2019-11-01" @default.
- W2999143107 modified "2023-09-27" @default.
- W2999143107 title "A Time-based ADC for Read-Modify-Write in Ferroelectric-FET Based Pseudo-Crossbar Arrays" @default.
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- W2999143107 doi "https://doi.org/10.1109/primeasia47521.2019.8950728" @default.
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