Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices
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DOI：
10.1109/iccad45719.2019.8942099
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Weidong Cao;Liu Ke;Ayan Chakrabarti;Xuan Zhang
中科院分区：
文献类型：
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作者：
Weidong Cao;Liu Ke;Ayan Chakrabarti;Xuan Zhang
最近的工作提出了神经网络（NN-）启发的模数转换器（NNADC），并证明了他们在许多新兴的应用中的巨大潜力。这些NNADC通常依赖于电阻式随机存取存储器（RRAM）器件来实现NN操作，并且需要高精度RRAM单元（6 × 12位）来实现中等量化分辨率（4 × 8位）。然而，RRAM阵列在大规模生产过程中的制造数据并不支持RRAM分辨率的这种乐观假设。在本文中，我们提出了一种基于低精度RRAM器件的NN启发的超分辨率ADC，利用协同设计方法，结合了流水线硬件架构与自定义NN训练框架。SPICE仿真结果表明，该方法能够有效地利用3位RRAM器件实现14位超分辨率ADC的鲁棒设计，并具有更高的功耗和速度性能以及具有竞争力的品质因数（FoM）。除了线性均匀量化之外，所提出的ADC还可以支持可配置的高分辨率非线性量化，具有高转换速度和低转换能量，为近传感器分析和处理提供未来的智能模拟到信息接口。
Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often rely on resistive random-access memory (RRAM) devices to realize the NN operations and require high-precision RRAM cells (6∼12-bit) to achieve a moderate quantization resolution (4∼8-bit). Such optimistic assumption of RRAM resolution, however, is not supported by fabrication data of RRAM arrays in large-scale production process. In this paper, we propose an NN-inspired super-resolution ADC based on low-precision RRAM devices by taking the advantage of a co-design methodology that combines a pipelined hardware architecture with a custom NN training framework. Results obtained from SPICE simulations demonstrate that our method leads to robust design of a 14-bit super-resolution ADC using 3-bit RRAM devices with improved power and speed performance and competitive figure-of-merits (FoMs). In addition to the linear uniform quantization, the proposed ADC can also support configurable high-resolution nonlinear quantization with high conversion speed and low conversion energy, enabling future intelligent analog-to-information interfaces for near-sensor analytics and processing.