While the human brain efficiently adapts to new tasks from a continuous stream of information, neural network models struggle to learn from sequential information without catastrophically forgetting previously learned tasks. This limitation presents a significant hurdle in deploying edge devices in real-world scenarios where information is presented in an inherently sequential manner. Active dendrites of pyramidal neurons play an important role in the brain ability to learn new tasks incrementally. By exploiting key properties of time-to-first-spike encoding and leveraging its high sparsity, we present a novel spiking neural network model enhanced with active dendrites. Our model can efficiently mitigate catastrophic forgetting in temporally-encoded SNNs, which we demonstrate with an end-of-training accuracy across tasks of 88.3% on the test set using the Split MNIST dataset. Furthermore, we provide a novel digital hardware architecture that paves the way for real-world deployment in edge devices. Using a Xilinx Zynq-7020 SoC FPGA, we demonstrate a 100-% match with our quantized software model, achieving an average inference time of 37.3 ms and an 80.0% accuracy.

翻译：尽管人脑能够持续从连续信息流中高效适应新任务，但神经网络模型在学习序列信息时仍面临灾难性遗忘先前所学任务的困境。这一局限性严重阻碍了边缘设备在呈现固有序列性的真实场景中的部署。锥体神经元的主动树突在大脑渐进式学习新任务的能力中扮演关键角色。通过利用时间到首次脉冲编码的核心特性并发挥其高稀疏性优势，我们提出了一种增强主动树突的新型脉冲神经网络模型。该模型能够有效缓解时序编码 SNN 中的灾难性遗忘问题——在分割MNIST数据集上的测试集跨任务准确率达88.3%。此外，我们创新性地设计了数字硬件架构，为边缘设备实际部署铺平道路。基于Xilinx Zynq-7020 SoC FPGA平台的验证表明，该硬件实现与量化软件模型完全匹配，平均推理时间仅37.3毫秒，准确率达到80.0%。