A variety of nonvolatile memory (NVM) devices including the resistive Random Access Memory (RRAM) are currently being investigated for implementing energy-efficient hardware for deep learning and artificial intelligence at the edge. RRAM devices are employed in the form of dense crosspoint or crossbar arrays. In order to exploit the high-density and low-power operation of these devices, circuit designers need to accommodate their nonideal behavior and consider their impact on circuit design and algorithm performance. Hybrid integration of RRAMs with standard CMOS technology is spurring the development of large-scale neuromorphic system-on-a-chip. This review article provides an overview of neuromorphic integrated circuits (ICs) using hybrid CMOS-RRAM integration with an emphasis on spiking neural networks (SNNs), device nonidealities, their associated circuit design challenges, and potential strategies for their mitigation. An overview of various SNN learning algorithms and their codevelopment with devices and circuits is discussed. Finally, a comparison of NVM-based fully integrated neuromorphic ICs is presented along with a discussion on their future evolution.

中文翻译：

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神经形态计算：从设备到集成电路

目前正在研究包括电阻随机存取存储器 (RRAM) 在内的各种非易失性存储器 (NVM) 设备，以在边缘实现用于深度学习和人工智能的节能硬件。RRAM 设备以密集交叉点或交叉阵列的形式使用。为了利用这些设备的高密度和低功耗操作，电路设计人员需要适应它们的非理想行为，并考虑它们对电路设计和算法性能的影响。RRAM 与标准 CMOS 技术的混合集成正在推动大规模神经形态片上系统的开发。这篇评论文章概述了使用混合 CMOS-RRAM 集成的神经形态集成电路 (IC)，重点是尖峰神经网络 (SNN)、设备非理想性、它们相关的电路设计挑战，以及缓解它们的潜在策略。讨论了各种 SNN 学习算法及其与设备和电路的共同开发的概述。最后，介绍了基于 NVM 的完全集成神经形态 IC 的比较，并讨论了它们的未来演变。