Active Dendrites Enable Efficient Continual Learning in Time-To-First-Spike Neural Networks
CoRR(2024)
摘要
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
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-
with our quantized software model, achieving an average inference time of 37.3
ms and an 80.0
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