6 papers
Multi-primitive in-memory computing for Monte Carlo tree search
Tergel Molom-Ochir, Benjamin F. Morris, Yintao He +6
Monte Carlo tree search (MCTS) enables artificial intelligence (AI) decision-making, but requires 55-300 W on conventional processors, limiting edge deployment. In-memory computing…
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…
End-to-End Transformer Acceleration Through Processing-in-Memory Architectures
Xiaoxuan Yang, Peilin Chen, Tergel Molom-Ochir +1
Transformers have become central to natural language processing and large language models, but their deployment at scale faces three major challenges. First, the attention mechanis…
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…
A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models
Cong Guo, Feng Cheng, Zhixu Du +21
The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language…