This paper investigates the relationship between mapping style and device roadmap in Resistive Random Access Memory (ReRAM) architectures for neuromorphic computing. The study leverages simulations using DNN+NeuroSim to evaluate the impact of different parameters on chip performance, including latency, energy consumption, and overall system efficiency. The results demonstrate that novel mapping techniques and a high-performance (HP) device roadmap are optimal if energy and speed considerations are weighted equally. This is because as the study demonstrates, HP devices provide a latency cut that outsizes the energy cost. Additionally, adopting novel mapping in the device cuts latency by nearly 30% while being slightly more energy efficient. The findings highlight the importance of considering mapping style and device roadmap in optimizing ReRAM architectures for neuromorphic computing, which may contribute to advancing the practical implementation of ReRAM in computational systems.

翻译：本文研究了神经形态计算中电阻式随机存取存储器（ReRAM）架构的映射风格与器件路线图之间的关系。研究利用DNN+NeuroSim仿真，评估了不同参数对芯片性能（包括延迟、能耗和系统整体效率）的影响。结果表明，若将能量与速度同等权重考虑，新颖的映射技术与高性能（HP）器件路线图最为理想。这是因为，如研究所示，HP器件带来的延迟缩减远超其能量开销。此外，在器件中采用新颖映射可实现延迟降低近30%，同时能效略有提升。这些发现强调了在优化面向神经形态计算的ReRAM架构时，考虑映射风格与器件路线图的重要性，这可能有助于推动ReRAM在计算系统中的实际应用。