PositCL: Compact Continual Learning with Posit Aware Quantization
Vedant Karia, Abdullah M. Zyarah, Dhireesha Kudithipudi · 2024
Neural network models catastrophically forget previously learned information while acquiring new knowledge, requiring a fundamental change in learning models and architectures. These enhancements to architecture structures and training mechanisms lead to an increase in memory and computational resources, making it difficult to deploy models on resource-constrained edge devices. To enhance both memory and computational efficiency, we propose a model compression approach for spiking continual learning models, where the model parameters are quantized with varying precision according to their weight distribution.