Compressing RNNs for IoT devices by 15-38x using Kronecker Products

7 June 2019

Papers citing "Compressing RNNs for IoT devices by 15-38x using Kronecker Products"

6 / 6 papers shown

Title
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices Mohamed Aboelenien Ahmed Kilian Pfeiffer R. Khalili Heba Khdr J. Henkel FedML 87 0 0 17 Feb 2025
TQCompressor: improving tensor decomposition methods in neural networks via permutations V. Abronin A. Naumov D. Mazur D. Bystrov K. Tsarova Ar. Melnikov Ivan V. Oseledets S. Dolgov R. Brasher M. Perelshtein 28 6 0 29 Jan 2024
From array algebra to energy efficiency on GPUs: Data and hardware shapes with dimension-lifting to optimize memory-processor layouts L. Mullin 14 0 0 19 Jun 2023
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems Martin Rapp R. Khalili Kilian Pfeiffer J. Henkel 19 18 0 16 Dec 2021
Kronecker Decomposition for GPT Compression Ali Edalati Marzieh S. Tahaei Ahmad Rashid V. Nia J. Clark Mehdi Rezagholizadeh 34 33 0 15 Oct 2021
Compacting Deep Neural Networks for Internet of Things: Methods and Applications Ke Zhang Hanbo Ying Hongning Dai Lin Li Yuangyuang Peng Keyi Guo Hongfang Yu 16 38 0 20 Mar 2021