  Autonomous embedded systems (e.g., robots) typically necessitate intelligent
computation with low power/energy processing for completing their tasks. Such
requirements can be fulfilled by embodied neuromorphic intelligence with
spiking neural networks (SNNs) because of their high learning quality (e.g.,
accuracy) and sparse computation. Here, the employment of event-based data is
preferred to ensure seamless connectivity between input and processing parts.
However, state-of-the-art SNNs still face a long training time to achieve high
accuracy, thereby incurring high energy consumption and producing a high rate
of carbon emission. Toward this, we propose FastSpiker, a novel methodology
that enables fast SNN training on event-based data through learning rate
enhancements targeting autonomous embedded systems. In FastSpiker, we first
investigate the impact of different learning rate policies and their values,
then select the ones that quickly offer high accuracy. Afterward, we explore
different settings for the selected learning rate policies to find the
appropriate policies through a statistical-based decision. Experimental results
show that our FastSpiker offers up to 10.5x faster training time and up to
88.39% lower carbon emission to achieve higher or comparable accuracy to the
state-of-the-art on the event-based automotive dataset (i.e., NCARS). In this
manner, our FastSpiker methodology paves the way for green and sustainable
computing in realizing embodied neuromorphic intelligence for autonomous
embedded systems.
