Structured state space models (SSM) have recently emerged as a new class of deep learning models, particularly well‐suited for processing long sequences. Their constant memory footprint, in contrast to the linearly scaling memory demands of transformers, makes them attractive candidates for deployment on resource‐constrained edge‐computing devices. While recent works have explored the effect of quantization‐aware training (QAT) on SSMs, they typically do not address its implications for specialized edge hardware, for example, analog in‐memory computing (AIMC) chips. In this work, it is demonstrated that QAT can significantly reduce the complexity of SSMs by up to two orders of magnitude across various performance metrics. The relation between model size and numerical precision is analyzed, and it is shown that QAT enhances robustness to analog noise and enables structural pruning. Finally, these techniques are integrated to deploy SSMs on a memristive AIMC substrate and highlight the resulting benefits in terms of computational efficiency.
Siegel et al. (Tue,) studied this question.