5 citations · 9 across the 8 of their papers we have counts for
3 papers · 1 filter
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity
Alessandro Pierro, Steven Abreu, Jonathan Timcheck +3
Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for s…
Mamba-PTQ: Outlier Channels in Recurrent Large Language Models
Alessandro Pierro, Steven Abreu
Modern recurrent layers are emerging as a promising path toward edge deployment of foundation models, especially in the context of large language models (LLMs). Compressing the who…
Q-S5: Towards Quantized State Space Models
Steven Abreu, Jens E. Pedersen, Kade M. Heckel +1
In the quest for next-generation sequence modeling architectures, State Space Models (SSMs) have emerged as a potent alternative to transformers, particularly for their computation…