Deploying to Speck2fDevKit

Introduction

This guide will show you how to deploy your pretrained model to a SynSense Speck2fDevkit with a SPECK^TM neuromorphic system-on-chip processor for both simulated event streams and real-time inferencing. This assumes you have already Trained and evaluted your own dataset, please follow this guide if you have not already done so.

Since this is specific for the Speck2fDevKit, no other SynSense boards will work. The callbacks for almost all of the functions for on-chip inferencing in this repo are specific for the Speck2fDevKit so must be modified to your board should you have a different model. Note though, we do not guarantee that it will work with any previous or future board models.

Getting started

With your board plugged in, please ensure you have set the necessary USB permissions for the Speck2fDevKit.

You will also need to install samna, which is not included in our conda-forge package.

# Install samna into your python environment
pip install samna
# Check the importation of samna works
python
> import samna

Note for samna, your Python version must not be greater than 3.11 which as of writing this guide is the highest version supported.

Evaluate your model on-chip using simulated event streams

Once everything is installed and you've checked it imports, we can now take a pretrained model and test its performance on chip. To do so, we'll add the --simulated_speck flag which tells LENS to generate simualted event streams, convert them into SPECK events, and send them to the Speck2fDevKit:

 python main.py \ 
--dataset <your dataset> \
--camera <your camera> \
--reference <your reference> \
--reference_places <number of reference places> \
--query <your query> \
--query_places <number of query places> \
--matching \
--simulated_speck

The model will collect the number of spikes generated in the output layer and generate a similarity matrix for each reference against every query.

Running the online inferencing model