  In the quest for next-generation sequence modeling architectures, State Space
Models (SSMs) have emerged as a potent alternative to transformers,
particularly for their computational efficiency and suitability for dynamical
systems. This paper investigates the effect of quantization on the S5 model to
understand its impact on model performance and to facilitate its deployment to
edge and resource-constrained platforms. Using quantization-aware training
(QAT) and post-training quantization (PTQ), we systematically evaluate the
quantization sensitivity of SSMs across different tasks like dynamical systems
modeling, Sequential MNIST (sMNIST) and most of the Long Range Arena (LRA). We
present fully quantized S5 models whose test accuracy drops less than 1% on
sMNIST and most of the LRA. We find that performance on most tasks degrades
significantly for recurrent weights below 8-bit precision, but that other
components can be compressed further without significant loss of performance.
Our results further show that PTQ only performs well on language-based LRA
tasks whereas all others require QAT. Our investigation provides necessary
insights for the continued development of efficient and hardware-optimized
SSMs.
