all AI news
NonGEMM Bench: Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads
April 19, 2024, 4:42 a.m. | Rachid Karami, Hemanth Kota, Sheng-Chun Kao, Hyoukjun Kwon
cs.LG updates on arXiv.org arxiv.org
Abstract: Machine Learning (ML) operators are the building blocks to design ML models with various target applications. GEneral Matrix Multiplication (GEMM) operators are the backbone of ML models. They are notorious for being computationally expensive requiring billions of multiply-and-accumulate. Therefore, significant effort has been put to study and optimize the GEMM operators in order to speed up the execution of ML models. GPUs and accelerators are widely deployed to accelerate ML workloads by optimizing the execution …
abstract applications arxiv building cs.ar cs.lg cs.pf design general horizon machine machine learning matrix matrix multiplication ml models operators performance type understanding workloads
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US