A High-Speed and High-Efficiency Diverse Error Margin Write-Verify Scheme for an RRAM-Based Neuromorphic Hardware Accelerator

Yudeng Lin, Jianshi Tang, Bin Gao, Qi Qin, Qingtian Zhang, He Qian, Huaqiang Wu

Abstract: Resistive random access memory (RRAM)-based neuromorphic hardware accelerators are attractive platforms for neural network acceleration due to their high energy efficiency. However, the inherent variations of RRAM, arising from diffusion or recombination of oxygen vacancies, can cause significant conductance deviation from the target value, resulting in noticeable performance degradation. In practical ex situ training, write-verify methods are widely adopted to avoid this issue when transferring a trained network model. However, the intense reading and reprogramming operations make the conventional write-verify methods require extensive programming time and energy. In this brief, for the first time, we propose a novel write-verify scheme that can transfer each weight with a different acceptable error margin to achieve a high-speed and high-efficiency write-verify scheme while maintaining network performance. Our experimental results show that the speed and energy efficiency of the write-verify process can be improved significantly, by up to $\mathbf {\times } 3.4\mathbf {\mathrm {\sim }} \mathbf {\times }9.0$ and $\mathbf {\times } 4.1\mathbf {\mathrm {\sim }} \mathbf {\times }14.1$ , respectively.