FPRaker: A Processing Element For Accelerating Neural Network Training

FPRaker: A Processing Element For Accelerating Neural Network Training

15 October 2020

Omar Mohamed Awad

Mostafa Mahmoud

Isak Edo Vivancos

Anand Jayarajan

Gennady Pekhimenko

Andreas Moshovos

Papers citing "FPRaker: A Processing Element For Accelerating Neural Network Training"

6 / 6 papers shown

Title
BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration Yuzong Chen Ahmed F. AbouElhamayed Xilai Dai Yang Wang Marta Andronic George A. Constantinides Mohamed S. Abdelfattah MQ 108 1 0 18 Nov 2024
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees Jue Wang Binhang Yuan Luka Rimanic Yongjun He Tri Dao Beidi Chen Christopher Ré Ce Zhang AI4CE 24 11 0 02 Jun 2022
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training Milovs Nikolić Enrique Torres Sanchez Jia-Hui Wang Ali Hadi Zadeh Mostafa Mahmoud Ameer Abdelhadi Kareem Ibrahim Andreas Moshovos MQ 33 1 0 28 Apr 2022
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators Lois Orosa Skanda Koppula Yaman Umuroglu Konstantinos Kanellopoulos Juan Gómez Luna Michaela Blott K. Vissers O. Mutlu 43 4 0 04 Feb 2022
Demystifying BERT: Implications for Accelerator Design Suchita Pati Shaizeen Aga Nuwan Jayasena Matthew D. Sinclair LLMAG 38 17 0 14 Apr 2021
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 245 1,826 0 17 Sep 2019