Quantization-Guided Training for Compact TinyML Models

10 March 2021

Papers citing "Quantization-Guided Training for Compact TinyML Models"

8 / 8 papers shown

Title
Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns Christos Kyrkou 39 0 0 20 Jul 2024
Extreme Compression of Adaptive Neural Images Leo Hoshikawa Marcos V. Conde Takeshi Ohashi Atsushi Irie 48 1 0 27 May 2024
LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum Learning Fatemeh Karimi Amir Mehrpanah Reza Rawassizadeh 19 1 0 16 Nov 2022
Deep learning model compression using network sensitivity and gradients M. Sakthi N. Yadla Raj Pawate 21 2 0 11 Oct 2022
Overcoming Oscillations in Quantization-Aware Training Markus Nagel Marios Fournarakis Yelysei Bondarenko Tijmen Blankevoort MQ 111 101 0 21 Mar 2022
An Empirical Study of Low Precision Quantization for TinyML Shaojie Zhuo Hongyu Chen R. Ramakrishnan Tommy Chen Chen Feng Yi-Rung Lin Parker Zhang Liang Shen MQ 32 13 0 10 Mar 2022
Implicit Neural Representations for Image Compression Yannick Strümpler Janis Postels Ren Yang Luc van Gool F. Tombari 24 158 0 08 Dec 2021
A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays Leonardo Ravaglia Manuele Rusci D. Nadalini Alessandro Capotondi Francesco Conti Luca Benini BDL 39 64 0 20 Oct 2021