This work introduces a spike-based wearable analytics system utilizing Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based on RRAM crossbars, which are known for their compactness and energy-efficiency. Given the hardware constraints and noise characteristics of the underlying RRAM crossbars, we propose online adaptation of pre-trained SNNs in real-time using Direct Feedback Alignment (DFA) against traditional backpropagation (BP). Direct Feedback Alignment (DFA) learning, that allows layer-parallel gradient computations, acts as a fast, energy & area-efficient method for online adaptation of SNNs on RRAM crossbars, unleashing better algorithmic performance against those adapted using BP. Through extensive simulations using our in-house hardware evaluation engine called DFA_Sim, we find that DFA achieves upto 64.1% lower energy consumption, 10.1% lower area overhead, and a 2.1x reduction in latency compared to BP, while delivering upto 7.55% higher inference accuracy on human activity recognition (HAR) tasks.

翻译：本研究提出了一种基于脉冲的可穿戴分析系统，该系统利用脉冲神经网络（SNN）部署在基于RRAM交叉阵列的内存计算引擎上，该引擎以其紧凑性和高能效而著称。考虑到底层RRAM交叉阵列的硬件约束和噪声特性，我们提出使用直接反馈对齐（DFA）替代传统的反向传播（BP），对预训练的SNN进行实时在线适应。直接反馈对齐（DFA）学习允许层并行梯度计算，作为一种快速、节能且面积高效的在线适应方法，用于在RRAM交叉阵列上部署SNN，相较于使用BP进行适应的方法，其算法性能更优。通过使用我们内部开发的硬件评估引擎DFA_Sim进行大量仿真，我们发现，在人体活动识别（HAR）任务上，与BP相比，DFA实现了高达64.1%的能耗降低、10.1%的面积开销减少以及2.1倍的延迟降低，同时推理准确率最高提升了7.55%。