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[DOC] VTA installation & basic tutorials (#47)
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vta/Makefile

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clean:
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$(RM) -rf build lib bin *~ */*~ */*/*~ */*/*/*~ */*.o */*/*.o */*/*/*.o
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$(RM) -rf cat.jpg quantize_graph.json quantize_params.pkl synset.txt
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-include build/*.d

vta/README.md

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It is not just an open-source hardware, but is an end to end solution that includes
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the entire software stack on top of VTA open-source hardware.
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The key features include:
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- Generic, modular open-source hardware
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- Streamlined workflow to deploy to FPGAs.
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- Simulator support
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- Driver and JIT runtime for both simulated backend and FPGA.
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- Simulator support to protoype compilation passes on regular workstations.
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- Driver and JIT runtime for both simulated and FPGA hardware backend.
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- End to end TVM stack integration
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- Direct optimization and deploy models from deep learning frameworks via TVM stack.
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- Customized and extendible TVM compiler backend
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- Flexible RPC support to ease the deployment, you can program it with python :)
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- Customized and extendible TVM compiler backend.
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- Flexible RPC support to ease the deployment, and program FPGAs with Python
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VTA is part of our effort on [TVM Stack](http://www.tvmlang.org/).
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VTA Installation
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----------------
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To get started with VTA, please follow the [Installation Guide](docs/how_to/install.md)
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ResNet-18 Inference Example
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---------------------------
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To offload ResNet-18 inference, follow the [ResNet-18 Guide](examples/resnet18/pynq/README.md)
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License
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-------
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© Contributors, 2018. Licensed under an [Apache-2.0](https://github.com/tmoreau89/vta/blob/master/LICENSE) license.

vta/apps/pynq_rpc/README.md

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#!/bin/bash
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export PYTHONPATH=${PYTHONPATH}:/home/xilinx/vta/nnvm/tvm/python:/home/xilinx/vta/python
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export PYTHONPATH=${PYTHONPATH}:/home/xilinx/vta/tvm/python:/home/xilinx/vta/python
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export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/opt/python3.6/lib/python3.6/site-packages/pynq/drivers/
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python -m vta.exec.rpc_server

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