9 citations · 9 across the 3 of their papers we have counts for
4 papers
Breaking the 1.58-bit Barrier for Ternary LLMs
Evangelos Georganas, Alexander Heinecke, Pradeep Dubey
Ternary Large Language Models (LLM) store every weight as one of three symbols , so the cost of a ternary model is conventionally referenced to the information-theoret…
Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simple
Evangelos Georganas, Alexander Heinecke, Pradeep Dubey
General Matrix Multiplication (GEMM) is the cornerstone of HPC workloads and Deep Learning. State-of-the-art (SOTA) vendor libraries tune tensor layouts, parallelization schemes an…
Pushing the Envelope of LLM Inference with Ultra-Low-Bit Quantized Models
Evangelos Georganas, Dhiraj Kalamkar, Alexander Heinecke +1
The advent of ultra-low-bit LLM models, approaching the perplexity and task accuracy of their full precision counterparts, is ushering in a new era of LLM inference. While these ad…
Microscaling Data Formats for Deep Learning
Bita Darvish Rouhani, Ritchie Zhao, Ankit More +30
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that…