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SDK setup

This is the latest version of the PULP SDK, which is under active development. The previous (now legacy) version, which is no longer supported, is on the v1 branch.

Getting started

These instructions were developed using a fresh Ubuntu 18.04 Bionic Beaver 64-Bit.

The following packages needed to be installed:

sudo apt-get install -y build-essential git libftdi-dev libftdi1 doxygen python3-pip libsdl2-dev curl cmake libusb-1.0-0-dev scons gtkwave libsndfile1-dev rsync autoconf automake texinfo libtool pkg-config libsdl2-ttf-dev

The SDK also requires the argcomplete Python package. You can install it for the local user with:

pip install --user argcomplete

Omit --user to install at system level instead, which will probably require admin rights.

This version requires PULP toolchain to compile the application exploiting pulp features. PULP toolchain is available at:

You can choose also its precompiled version, exploring:

Please, refer to the corresponding README for the installation.

Once PULP toolchain is correctly installed, define the path in which there is toolchain bin folder:


Source the file corresponding to the desired configuration:

cd pulp-sdk
source configs/

At least gcc 4.9.1 is needed. If the default one is not correct, CC and CXX can be set to point to a correct one. To check if gcc has the right version:

gcc --version

Please, refer to official guide to update gcc if is needed.

SDK build

Compile the SDK with this command:

make build

Test execution

Some examples are availaible at

Then, go to a test, for example pmsis_tests/quick/cluster/fork/, and execute:

make clean all run

This will by default execute it on gvsoc (platform=gvsoc), and you can configure the RTL platform with this command:

make clean all run platform=rtl

Notice that the environment variable VSIM_PATH should be set to the directory where the RTL platform has been built. This is typically done by sourcing the setup/ file from the main folder of the RTL platform.

Application: CNNs at the Edge

To run pre-generated real-world networks, such as MobileNetV1:

cd applications/MobileNetV1
make clean all run platform=<PLATFORM> CORE=<NUM_CORES>

Nemo + Dory + Pulp-NN

Our vertical flow allows to deploy optimized QNNs on low-power and low-resources MCUs, starting from a Pytorch model.


[Nemo] is a framework for Deep Neural Networks layer-wise quantization. He starts from a common Pytorch project and produces an equivalent quantized model, which well suits the usually integer MCUs. Its output are a .onnx as quantized model and several .txt as set of input and weigths of the network, also including the golden activations to checks the output of every network's layer. Please refer to its README for more details and [here] you can find a Colab project and a very detailed tutorial on how to get started with Nemo.


[Dory] is an automatic tool to generate and directly deploy MLP/CNNs on Pulp family boards, exploiting [Pulp-NN] as optimized back-end.

Dory has a complete and autonomous testsuite, named [Dory-Example], which is periodically updated, and please refer to its README for more details. To generate the code and run one of these examples:

cd dory/dory_examples/
python3 network_generate --network_dir <e.g., ./examples/MobileNetV1/>
cd application
make clean all run platform=<PLATFORM> CORE=<NUM_CORES>

where you should choose CORE=8 if you want to test the network on pulp cluster with all of the eight cores active (by default only 1 is set).

To set up and execute a custom application, firstly, copy your file network.onnx and files out_layer{i}.txt in a single folder (e.g., pulp-sdk/application/MyCustomNetwork/) and then:

cd dory/dory_examples/
python3 network_generate --network_dir <pulp-sdk/application/MyCustomNetwork/>
cd application
make clean all run platform=<PLATFORM> CORE=<NUM_CORES>

You can use L1 and L2 memory constraints to specify the amount of memory used inside the application. Please refer to Dory and Dory-example READMEs for more details.