Mixed-Signal Neuromorphic Computing Circuits Using Hybrid CMOS-RRAM Integration
Mixed-Signal Neuromorphic Computing Circuits Using Hybrid CMOS-RRAM Integration
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DOI：
10.1109/tcsii.2020.3048034
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发表时间：
2021-02
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影响因子：
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通讯作者：
V. Saxena
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文献类型：
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作者：
V. Saxena
最近电阻式随机存取存储器（RRAM）与标准CMOS的集成刺激了对高密度和低功率存储器内计算的探索。RRAM阵列正被广泛研究用于模拟域向量矩阵乘法（VMM）和神经形态计算。然而，为了利用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器件的非理想性，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。这篇简报回顾了使用混合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，随后对最近的演示进行了基准测试。
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.