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Main README reflects SeeDot
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@ -16,6 +16,7 @@ This repository contains algorithms that shine in this setting in terms of both
- **ProtoNN**: **Proto**type based k-nearest neighbors (k**NN**) classifier. - **ProtoNN**: **Proto**type based k-nearest neighbors (k**NN**) classifier.
- **EMI-RNN**: Training routine to recover the critical signature from time series data for faster and accurate RNN predictions. - **EMI-RNN**: Training routine to recover the critical signature from time series data for faster and accurate RNN predictions.
- **FastRNN & FastGRNN - FastCells**: **F**ast, **A**ccurate, **S**table and **T**iny (**G**ated) RNN cells. - **FastRNN & FastGRNN - FastCells**: **F**ast, **A**ccurate, **S**table and **T**iny (**G**ated) RNN cells.
- **SeeDot**: Floating-point to fixed-point quantization tool.
These algorithms can train models for classical supervised learning problems These algorithms can train models for classical supervised learning problems
with memory requirements that are orders of magnitude lower than other modern with memory requirements that are orders of magnitude lower than other modern
@ -27,14 +28,16 @@ The `tf` directory contains code, examples and scripts for all these algorithms
in TensorFlow. The `cpp` directory has training and inference code for Bonsai and in TensorFlow. The `cpp` directory has training and inference code for Bonsai and
ProtoNN algorithms in C++. Please see install/run instruction in the Readme ProtoNN algorithms in C++. Please see install/run instruction in the Readme
pages within these directories. The `applications` directory has code/demonstrations pages within these directories. The `applications` directory has code/demonstrations
of applications of the EdgeML algorithms. of applications of the EdgeML algorithms. The `Tools/SeeDot` directory has the
quantization tool to generate fixed-point inference code.
For details, please see our [wiki For details, please see our [wiki
page](https://github.com/Microsoft/EdgeML/wiki/) and our ICML'17 publications page](https://github.com/Microsoft/EdgeML/wiki/) and our ICML'17 publications
on [Bonsai](docs/publications/Bonsai.pdf) and on [Bonsai](docs/publications/Bonsai.pdf) and
[ProtoNN](docs/publications/ProtoNN.pdf) algorithms, NIPS'18 publications on [ProtoNN](docs/publications/ProtoNN.pdf) algorithms, NeurIPS'18 publications on
[EMI-RNN](docs/publications/emi-rnn-nips18.pdf) and [EMI-RNN](docs/publications/emi-rnn-nips18.pdf) and
[FastGRNN](docs/publications/FastGRNN.pdf). [FastGRNN](docs/publications/FastGRNN.pdf), PLDI'19 publication on
[SeeDot](docs/publications/SeeDot.pdf).
Core Contributors: Core Contributors:
@ -44,6 +47,7 @@ Core Contributors:
- [Don Dennis](https://dkdennis.xyz) - [Don Dennis](https://dkdennis.xyz)
- [Harsha Vardhan Simhadri](http://harsha-simhadri.org) - [Harsha Vardhan Simhadri](http://harsha-simhadri.org)
- [Shishir Patil](https://shishirpatil.github.io/) - [Shishir Patil](https://shishirpatil.github.io/)
- [Sridhar Gopinath](http://www.sridhargopinath.in/)
We welcome contributions, comments, and criticism. For questions, please [email We welcome contributions, comments, and criticism. For questions, please [email
Harsha](mailto:harshasi@microsoft.com). Harsha](mailto:harshasi@microsoft.com).

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