From 67e1652ae981152c61553893c99ec3200f45b358 Mon Sep 17 00:00:00 2001 From: Wei-Chen Wang Date: Wed, 6 Mar 2024 16:05:58 -0500 Subject: [PATCH] Update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5e176452..98a063ed 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ TinyEngine is a part of MCUNet, which also consists of TinyNAS. MCUNet is a syst - **(2022/11)** We release the source code of Tiny Training Engine, and include the [tutorial of our training demo](tutorial/training) for training a visual wake words (VWW) model on microcontrollers. - **(2022/11)** We release the source code of the algorithm and compilation parts of MCUNetV3 in [this repo](https://github.com/mit-han-lab/tiny-training). Please take a look! - **(2022/10)** Our new work [On-Device Training Under 256KB Memory](https://arxiv.org/abs/2206.15472) is highlighted on the [MIT homepage](http://web.mit.edu/spotlight/learning-edge/)! -- **(2022/09)** Our new work [On-Device Training Under 256KB Memory](https://arxiv.org/abs/2206.15472) is accepted to NeurIPS 2022! It enables tiny on-device training for IoT devices \[[demo](https://www.youtube.com/watch?v=XaDCO8YtmBw)\]. +- **(2022/09)** Our new work [On-Device Training Under 256KB Memory](https://arxiv.org/abs/2206.15472) is accepted to NeurIPS 2022! It enables tiny on-device training for IoT devices. - **(2022/08)** Our **New Course on TinyML and Efficient Deep Learning** will be released soon in September 2022: [efficientml.ai](https://efficientml.ai/). - **(2022/08)** We include the [tutorial of our inference demo](tutorial/inference) for deploying a visual wake words (VWW) model onto microcontrollers. - **(2022/08)** We opensource the TinyEngine repo.