5 papers
CAMformer: Associative Memory is All You Need
Tergel Molom-Ochir, Benjamin F. Morris, Mark Horton +8
Transformers face scalability challenges due to the quadratic cost of attention, which involves dense similarity computations between queries and keys. We propose CAMformer, a nove…
Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers
Mark Horton, Tergel Molom-Ochir, Peter Liu +8
Pre-trained transformer models with extended context windows are notoriously expensive to run at scale, often limiting real-world deployment due to their high computational and mem…
MonoSparse-CAM: Efficient Tree Model Processing via Monotonicity and Sparsity in CAMs
Tergel Molom-Ochir, Brady Taylor, Hai Li +1
While the tree-based machine learning (TBML) models exhibit superior performance compared to neural networks on tabular data and hold promise for energy-efficient acceleration usin…
Biologically Plausible Learning on Neuromorphic Hardware Architectures
Christopher Wolters, Brady Taylor, Edward Hanson +3
With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data mov…
Neuromorphic Algorithm-hardware Codesign for Temporal Pattern Learning
Haowen Fang, Brady Taylor, Ziru Li +3
Neuromorphic computing and spiking neural networks (SNN) mimic the behavior of biological systems and have drawn interest for their potential to perform cognitive tasks with high e…