2 papers
cs.LG2025
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
Sebastian Siegel, Ming-Jay Yang, Younes Bouhadjar +3
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 foot…
cs.LG2024
IMSSA: Deploying modern state-space models on memristive in-memory compute hardware
Sebastian Siegel, Ming-Jay Yang, John-Paul Strachan
Processing long temporal sequences is a key challenge in deep learning. In recent years, Transformers have become state-of-the-art for this task, but suffer from excessive memory r…