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.

翻译：最近的进步表明,基于 SNN 的系统可以高效地进行不受监督的连续学习,因为其生物可接受学习规则,例如Spik-Timing-Depported可塑性(STDP)等。这种学习能力对于诸如自主代理(如机器人和UAVs)等需要不断适应动态变化的情景/环境,需要不断适应动态变化的情景/环境,而从环境直接收集的新数据可能具有新特点,因此可以在网上学习。目前,以SNNNE为主的精度工程在培训和发酵阶段都采用高精度的参数(即,32位),从而带来高的内存和能源成本,从而妨碍在电池驱动的移动自主系统(SST)。另一方面,精确度降低可能危及由于信息损失而需要持续不断不断学习的质量。为此,我们建议使用 lpSpikikeCon, 一种新模式,使低精度 SNNNE处理能够通过不受控制的自动代理代理/系统进行高效的不断学习。我们IPSBniflical 的精度学习系统,在Sniflical Deal Stal Studal Delearnial Deal Deal Dele lading 方法下进行一项关键步骤分析。