Search Results for author: Bilge Acun

Found 10 papers, 2 papers with code

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale

no code implementations11 Nov 2020 Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim Hazelwood

The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models.

TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models

1 code implementation25 Jan 2021 Chunxing Yin, Bilge Acun, Xing Liu, Carole-Jean Wu

TT-Rec achieves 117 times and 112 times model size compression, for Kaggle and Terabyte, respectively.

RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation

no code implementations25 Jan 2022 Geet Sethi, Bilge Acun, Niket Agarwal, Christos Kozyrakis, Caroline Trippel, Carole-Jean Wu

EMBs exhibit distinct memory characteristics, providing performance optimization opportunities for intelligent EMB partitioning and placement across a tiered memory hierarchy.

MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation

no code implementations21 Feb 2023 Samuel Hsia, Udit Gupta, Bilge Acun, Newsha Ardalani, Pan Zhong, Gu-Yeon Wei, David Brooks, Carole-Jean Wu

Based on our characterization of various embedding representations, we propose a hybrid embedding representation that achieves higher quality embeddings at the cost of increased memory and compute requirements.

Recommendation Systems

Data Acquisition: A New Frontier in Data-centric AI

no code implementations22 Nov 2023 Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou

As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative.

Generative AI Beyond LLMs: System Implications of Multi-Modal Generation

no code implementations22 Dec 2023 Alicia Golden, Samuel Hsia, Fei Sun, Bilge Acun, Basil Hosmer, Yejin Lee, Zachary DeVito, Jeff Johnson, Gu-Yeon Wei, David Brooks, Carole-Jean Wu

As the development of large-scale Generative AI models evolve beyond text (1D) generation to include image (2D) and video (3D) generation, processing spatial and temporal information presents unique challenges to quality, performance, and efficiency.

Cannot find the paper you are looking for? You can Submit a new open access paper.