We propose, for the first time, an indium gallium zinc oxide (IGZO)-based 2T1C compute cell (IGZO-cell) for analog in-memory computing. To assess the impact of an IGZO-cell-based array including the periphery on power and accuracy, a PyTorch framework was developed to analytically modeled analog components. The results are reported for a ResNet20 network on the Canadian Institute For Advanced Research-10 (CIFAR-10) benchmark. The state-of-the-art energy efficiency of 15 peta operations per second (POPS)/W including the periphery is achieved by using our proposed IGZO-cell with CMOS compatibility. Finally, it is shown that, with a properly trained neural network model, there is no degradation of test accuracy with 10% device to device variability for the IGZO devices.

中文翻译：

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用于模拟内存计算的基于 IGZO 的计算单元——DTCO 分析以在边缘实现超低功耗 AI

我们首次提出了一种基于铟镓锌氧化物 (IGZO) 的 2T1C 计算单元 (IGZO-cell)，用于模拟内存计算。为了评估基于 IGZO 单元的阵列（包括外围设备）对功率和精度的影响，开发了 PyTorch 框架以对模拟组件进行分析建模。结果是在加拿大高级研究所 10 (CIFAR-10) 基准测试中针对 ResNet20 网络报告的。通过使用我们提出的具有 CMOS 兼容性的 IGZO 单元，实现了包括外围在内的每秒 15 peta 操作 (POPS)/W 的最先进能效。最后，结果表明，使用经过适当训练的神经网络模型，IGZO 设备的设备间可变性为 10% 时，测试精度不会降低。