Recent integration of Resistive Random Access Memory (RRAM) with standard CMOS has spurred exploration of high-density and low-power in-memory computing. RRAM arrays are being intensely investigated for analog-domain Vector Matrix Multiplication (VMM) and Neuromorphic Computing. However, to exploit the advantages of RRAM over other forms of nonvolatile memories, mixed-signal circuit designers need to accommodate their device nonidealities, and design circuits to translate high-level deep neural network algorithms to mixed-signal hardware. This brief reviews the field of neuromorphic computing using hybrid CMOS-RRAM circuits, associated circuit design challenges, and potential approaches for their mitigation, followed by benchmarking of recent demonstrations.

中文翻译：

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使用混合CMOS-RRAM集成的混合信号神经形态计算电路

电阻随机存取存储器（RRAM）与标准CMOS的最新集成刺激了对高密度和低功耗内存计算的探索。RRAM阵列正在为模拟域矢量矩阵乘法（VMM）和神经形态计算进行深入研究。但是，要利用RRAM相对于其他形式的非易失性存储器的优势，混合信号电路设计人员需要适应其设备的非理想性，并设计电路以将高级深度神经网络算法转换为混合信号硬件。本文简要介绍了使用混合CMOS-RRAM电路的神经形态计算领域，相关的电路设计挑战以及缓解这些挑战的潜在方法，然后对最近的演示进行了基准测试。