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- 摘要
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- 实验方案
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A 12.8-Gb/s Daisy Chain-Based Downlink I/F Employing Spectrally Compressed Multi-Band Multiplexing for High-Bandwidth, Large-Capacity Storage Systems
摘要： Toward the aim of realizing high-bandwidth, large-capacity nand flash memory-based storage systems, this paper presents a novel daisy-chain downlink interface (I/F) between a controller and a large number of nand packages. The daisy-chain I/F employs a tapered-bandwidth architecture with bridge-by-bridge data extraction based on a proposed spectrally compressed multi-band multiplexing (SCM2) technique to achieve low power consumption. By using the proposed downlink I/F, a nand controller can handle 32 low-cost nand packages while achieving 12.8-Gb/s throughput using two data wires. The fabricated prototype downlink I/F achieved a bit error rate (BER) of 10^-12 with power consumption of 252.1 mW for the transmitter and 375.7 mW for all four receivers together.
关键词： large capacity storage,inter-symbol interference (ISI),daisy chain,Toggle double data rate (DDR),multi-band multiplexing,inter-channel interference (ICI),high-speed I/F,Bridge interface (I/F),high bandwidth,NAND flash memory,multi-drop bus
更新于2025-09-23 15:22:29
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Microcavity characteristics analysis of micro-shuttered organic light-emitting diodes
摘要： We propose a designing of multi-layer neural networks using 2D NAND flash memory cell as a high-density and reliable synaptic device. Our operation scheme eliminates the waste of NAND flash cells and allows analogue input values. A 3-layer perceptron network with 40,545 synapses is trained on a MNIST database set using an adaptive weight update method for hardware-based multi-layer neural networks. The conductance response of NAND flash cells is measured and it is shown that the unidirectional conductance response is suitable for implementing multi-layer neural networks using NAND flash memory cells as synaptic devices. Using an online-learning, we obtained higher learning accuracy with NAND synaptic devices compared to that with a memristor-based synapse regardless of weight update methods. Using an adaptive weight update method based on a unidirectional conductance response, we obtained a 94.19% learning accuracy with NAND synaptic devices. This accuracy is comparable to 94.69% obtained by synapses based on the ideal perfect linear device. Therefore, NAND flash memory which is mature technology and has great advantage in cell density can be a promising synaptic device for implementing high-density multi-layer neural networks.
关键词： synaptic device,multi-layer neural networks,hardware-based neural network,deep neural networks (DNNs),deep learning,NAND flash memory,neuromorphic
更新于2025-09-11 14:15:04