Create customized SNN code

General
11

Hello @raul.parada.medina , conversion of a model from BrainChip’s model zoo into our format is straightforward:

Once you have an Akida model object, like Step 4 in the example:
model_akida = convert(model_quantized_keras)

You’ll need to map the model to your device and then convert it to a compatible binary:

from akida import devices

# Map model onto your Akida device
dev = devices()[0]
try:
    model_akida.map(dev, hw_only=True)
except RuntimeError:
    model_akida.map(dev, hw_only=False)

# Extract the C++-compatible program blob
blob = model_akida.sequences[0].program
with open("model_cxx.fbz", "wb") as f:
    f.write(blob)

print("C++-compatible model written to model_cxx.fbz")

Note: You want to be sure that the model is supported on your Akida device. There are many models on the BrainChip model zoo that are not compatible with their “version 1 IP” devices.

If your device is a v1 device, you’ll need to add a set_akida_version guard:

from cnn2snn import convert, set_akida_version, AkidaVersion
# Convert the model
with set_akida_version(AkidaVersion.v1):
    model_akida = convert(model_quantized_keras)
    model_akida.summary()

    from akida import devices
    # Map model onto your Akida device
    # ... (see above)

for more information on v1/v2 model compatibility please see their docs: Akida models zoo — Akida Examples documentation

Once you have a model binary blob created:

Create a model JSON file adjacent to the blob by following Model JSON Structure | DeGirum Docs or by looking at existing BrainChip models on our AI Hub for reference: https://hub.degirum.com/degirum/brainchip

ModelPath is your binary model file
RuntimeAgent is AKIDA
DeviceType is the middle output from akida devices in all caps.
For example for if akida devices shows: PCIe/NSoC_v2/0 you put: NSOC_V2

Your JSON + binary model blob are now compatible with PySDK. Try running the inference on your device locally by specifying the full path to the JSON as a zoo_url, see: PySDK Package | DeGirum Docs
“For local AI hardware inferences you specify zoo_url parameter as either a path to a local model zoo directory, or a path to model’s .json configuration file.”

You can then zip them up and upload them to your model zoo in our AI Hub.

Let me know if this helped.

P.S. we currently have v1 hardware in our cloud farm, and this model is the face estimation model for NSoC_v2: https://hub.degirum.com/degirum/brainchip/vgg_regress_age_utkface--32x32_quant_akida_NSoC_1