The slowdown of the CMOS technology scaling, and the trade-off between efficiency and flexibility have fueled the exploration into novel architectures with emerging post-CMOS technology [e.g., resistive-RAM (RRAM)]. In this article, a nonvolatile fully programmable processing-in-memory (PIM) processor named Liquid Silicon is demonstrated, which combines the superior programmability of general-purpose computing devices [e.g., field-programmable gate array (FPGA)] and the high efficiency of domain-specific accelerators. Besides the general computing applications, Liquid Silicon is particularly well suited for artificial intelligence (AI)/machine learning and big data applications, which not only poses high computational/memory demand but also evolves rapidly. To fabricate the Liquid Silicon chip, the HfO₂ RRAM is monolithically integrated on top of the commercial 130 nm CMOS. Our measurement confirms that Liquid Silicon chip can operate reliably at a low voltage of 650 mV. It achieves 60.9 TOPS/W in performing neural network (NN) inferences, and 480 GOPS/W in performing content-based similarity search (a key big data application) at a nominal voltage supply of 1.2 V, showing $3\times $ and $100\times $ improvement over the state-of-the-art domain-specific CMOS-/RRAM-based accelerators. In addition, it outperforms the latest nonvolatile FPGA in energy efficiency by $3\times $ in general computing applications.

中文翻译：

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液态硅：具有单片集成 ReRAM 的非易失性完全可编程内存处理器

CMOS 技术缩放的放缓以及效率和灵活性之间的权衡推动了对具有新兴后 CMOS 技术的新型架构的探索 [例如，电阻 RAM (RRAM)]。在本文中，展示了一种名为 Liquid Silicon 的非易失性完全可编程内存处理 (PIM) 处理器，它结合了通用计算设备 [例如现场可编程门阵列 (FPGA)] 的卓越可编程性和高效率特定领域的加速器。除了通用计算应用，Liquid Silicon特别适合人工智能（AI）/机器学习和大数据应用，这些应用不仅对计算/内存的需求高，而且发展迅速。为了制造液态硅芯片，HfO ₂RRAM 单片集成在商用 130 nm CMOS 之上。我们的测量证实，液态硅芯片可以在 650 mV 的低电压下可靠运行。它在执行神经网络 (NN) 推理时达到 60.9 TOPS/W，在 1.2 V 标称电源电压下执行基于内容的相似性搜索（一个关键的大数据应用）时达到 480 GOPS/W，显示 $3\times $ 和 $100\次 $ 对最先进的特定领域 CMOS-/RRAM 加速器的改进。此外，它在能效方面优于最新的非易失性 FPGA $3\times $ 在一般计算应用程序中。