lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents. (arXiv:2205.12295v1 [cs.NE])

Recent advances have shown that SNN-based systems can efficiently perform
unsupervised continual learning due to their bio-plausible learning rule, e.g.,
Spike-Timing-Dependent Plasticity (STDP). Such learning capabilities are
especially beneficial for use cases like autonomous agents (e.g., robots and
UAVs) that need to continuously adapt to dynamically changing
scenarios/environments, where new data gathered directly from the environment
may have novel features that should be learned online. Current state-of-the-art
works employ high-precision weights (i.e., 32 bit) for both training and
inference phases, which pose high memory and energy costs thereby hindering
efficient embedded implementations of such systems for battery-driven mobile
autonomous systems. On the other hand, precision reduction may jeopardize the
quality of unsupervised continual learning due to information loss. Towards
this, we propose lpSpikeCon, a novel methodology to enable low-precision SNN
processing for efficient unsupervised continual learning on
resource-constrained autonomous agents/systems. Our lpSpikeCon methodology
employs the following key steps: (1) analyzing the impacts of training the SNN
model under unsupervised continual learning settings with reduced weight
precision on the inference accuracy; (2) leveraging this study to identify SNN
parameters that have a significant impact on the inference accuracy; and (3)
developing an algorithm for searching the respective SNN parameter values that
improve the quality of unsupervised continual learning. The experimental
results show that our lpSpikeCon can reduce weight memory of the SNN model by
8x (i.e., by judiciously employing 4-bit weights) for performing online
training with unsupervised continual learning and achieve no accuracy loss in
the inference phase, as compared to the baseline model with 32-bit weights
across different network sizes.

Source: https://arxiv.org/abs/2205.12295


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