3 citations · 11 across the 9 of their papers we have counts for
4 papers · 1 filter
How Far Can Disaggregation Go? A Design-Space Exploration of Attention-FFN Disaggregation for Efficient MoE LLM Serving
Hanjiang Wu, Abhimanyu Rajeshkumar Bambhaniya, Sarbartha Banerjee +9
Modern large language model (LLM) inference has progressively disaggregated to keep pace with growing model sizes and tight TTFT and TPOT service-level objectives: from chunked-pre…
Scaling Multi-Node Mixture-of-Experts Inference Using Expert Activation Patterns
Abhimanyu Bambhaniya, Geonhwa Jeong, Jason Park +6
Most recent state-of-the-art (SOTA) large language models (LLMs) use Mixture-of-Experts (MoE) architectures to scale model capacity without proportional per-token compute, enabling…
Enabling Unstructured Sparse Acceleration on Structured Sparse Accelerators
Geonhwa Jeong, Po-An Tsai, Abhimanyu R. Bambhaniya +2
Exploiting sparsity in deep neural networks (DNNs) has been a promising area for meeting the growing computation requirements. To minimize the overhead of sparse acceleration, hard…
Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers
Abhimanyu Rajeshkumar Bambhaniya, Amir Yazdanbakhsh, Suvinay Subramanian +4
N:M Structured sparsity has garnered significant interest as a result of relatively modest overhead and improved efficiency. Additionally, this form of sparsity holds considerable…