SCOLAR: A Spiking Digital Accelerator with Dual Fixed Point for Continual Learning

Spiking neural network models when deployed in dynamic environments, catastrophically forget previously learned tasks. In this paper, we propose a reconfigurable spiking digital accelerator, which uses activity-dependent metaplasticity to mitigate catastrophic forgetting. The proposed accelerator has a custom low precision dual fixed point representation for network parameters. The custom precision leads to lower quantization error and higher accuracy. We evaluate the proposed accelerator on split-MNIST continual learning benchmark. Analysis shows that representing network parameters with 8-bit dual fixed point numbers reduces the memory footprint compared to 16-bit fixed point numbers, while maintaining comparable continual learning ability.