Complete machine learning framework targets neuromorphic SoC

July 25, 2018 // By Julien Happich
Neuromorphic computing company BrainChip has released the Akida Development Environment, a machine learning framework for the creation, training, and testing of spiking neural networks (SNNs), supporting the development of systems for edge and enterprise products on the Company’s Akida Neuromorphic System-on-Chip (NSoC).

The Akida Development Environment includes the Akida Execution Engine, data-to-spike converters, and a model zoo of pre-created spiking neural network (SNN) models. The framework leverages the Python scripting language and its associated tools and libraries, including Jupyter notebooks, NumPy and Matplotlib.

The Akida Execution Engine is at the center of the framework and contains a software simulation of the Akida neuron, synapses, and the multiple supported training methodologies. Easily accessed through API calls in a Python script, users can specify their neural network topologies, training method, and datasets for execution. Based on the structure of the Akida neuron, the execution engine supports multiple training methods, including unsupervised training and unsupervised training with a labelled final layer. 

Spiking neural networks work on spike patterns. The development environment natively accepts spiking data created by Dynamic Vision Sensors (DVS). However, there are many other types of data that can be used with SNNs. Embedded in the Akida Execution Engine are data-to-spike converters, which convert common data formats such as image information (pixels) into the spikes required for an SNN. The development environment will initially ship with a pixel-to-spike data converter, to be followed by converters for audio and big data requirements in cybersecurity, financial information and the Internet-of-Things data. Users are also able to create their own proprietary data to spike converters to be used within the development environment. 


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