DenRAM：面向带延迟的高效时序处理的、基于RRAM的神经形态树突架构
DenRAM: Neuromorphic Dendritic Architecture with RRAM for Efficient Temporal Processing with Delays
AI总结：
本研究提出首款结合RRAM与130nm工艺的模拟树突脉冲神经网络架构DenRAM，利用RRAM同时实现突触延迟与权重，可高效完成时空模式识别，抗噪声能力强、准确率优于同参数量循环架构，为低功耗实时信号处理提供新方案。
摘要：
越来越多的神经科学研究强调，大脑锥体神经元的空间树突分支通过局部突触整合支持非线性计算，其重要性日益凸显。具体而言，树突分支在时序信号处理和特征检测中发挥关键作用，这依赖于重合检测（coincidence detection, CD）机制；而突触延迟的存在能够让时间上不同步的输入信号对齐，从而实现有效整合，为重合检测提供了基础。针对脉冲神经网络的计算研究进一步凸显了延迟对重合检测操作的重要性——它让前馈神经网络无需循环架构即可实现时空模式识别。\n在本研究中，我们提出DenRAM——这是首个集成模拟树突电路的脉冲神经网络实现，采用130nm工艺节点结合阻变存储器（resistive memory, RRAM）技术打造。DenRAM的树突电路利用RRAM器件同时实现网络中的延迟和突触权重。通过配置RRAM器件复现符合生物真实感的时间尺度，并利用器件的异质性，我们通过实验证明DenRAM能够复现突触延迟分布，并且能高效实现重合检测以完成时空模式识别。\n为验证该架构，我们在两个代表性时序基准上开展了全面的系统级仿真，结果表明DenRAM对模拟硬件噪声具有鲁棒性，且与参数数量相当的循环架构相比，准确率更优。DenRAM不仅为神经形态架构带来了丰富的时序处理能力，还降低了边缘设备的内存占用，在时序基准上实现了高精度，是低功耗实时信号处理技术的重要进展。
英文摘要：
An increasing number of neuroscience studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the brain for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection, using coincidence detection (CD) mechanisms, made possible by the presence of synaptic delays that align temporally disparate inputs for effective integration. Computational studies on spiking neural networks further highlight the significance of delays for CD operations, enabling spatio-temporal pattern recognition within feed-forward neural networks without the need for recurrent architectures. In this work, we present DenRAM, the first realization of a spiking neural network with analog dendritic circuits, integrated into a 130nm technology node coupled with resistive memory (RRAM) technology. DenRAM's dendritic circuits use the RRAM devices to implement both delays and synaptic weights in the network. By configuring the RRAM devices to reproduce bio-realistic timescales, and through exploiting their heterogeneity, we experimentally demonstrate DenRAM's capability to replicate synaptic delay profiles, and efficiently implement CD for spatio-temporal pattern recognition. To validate the architecture, we conduct comprehensive system-level simulations on two representative temporal benchmarks, highlighting DenRAM's resilience to analog hardware noise, and its superior accuracy compared to recurrent architectures with an equivalent number of parameters. DenRAM not only brings rich temporal processing capabilities to neuromorphic architectures, but also reduces the memory footprint of edge devices, provides high accuracy on temporal benchmarks, and represents a significant step-forward in low-power real-time signal processing technologies.
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