71 citations · 73 across the 6 of their papers we have counts for
6 papers
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…
Neuromorphic Programming: Emerging Directions for Brain-Inspired Hardware
Steven Abreu, Jens E. Pedersen
The value of brain-inspired neuromorphic computers critically depends on our ability to program them for relevant tasks. Currently, neuromorphic hardware often relies on machine le…
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…
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Jens E. Pedersen, Steven Abreu, Matthias Jobst +12
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine…
Concepts and Paradigms for Neuromorphic Programming
Steven Abreu
The value of neuromorphic computers depends crucially on our ability to program them for relevant tasks. Currently, neuromorphic computers are mostly limited to machine learning me…