Update readme.md

cpldcpu · web-flow · commit 2f1f6666c84d · 2024-04-30T18:05:54.000+02:00
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 **BitNetMCU** is a project focused on the training and inference of low-bit quantized neural networks, specifically designed to run efficiently on low-end RISC-V microcontrollers like the CH32V003. Quantization aware training (QAT) and finetuning of model structure and inference code allowed *surpassing 99% Test accuracy on a 16x16 MNIST dataset without using multiplication instructions and in only 2kb of RAM and 16kb of Flash*.
 
-The training pipeline is based in PyTorch and should run anywhere. The inference engine is implemented in Ansi-C and can be easily ported to any Microcontroller.
+The training pipeline is based on PyTorch and should run anywhere. The inference engine is implemented in Ansi-C and can be easily ported to any Microcontroller.
 
 **You can find a detailed report on the project in the `docs/` directory [here](docs/documentation.md).**
 
