BrainChip makes Akida Development Environment available
- 저자:Ella Cai
- 에 출시:2018-07-26
BrainChip is offering access to its Akida Development Environment – a machine learning framework for the creation, training, and testing of spiking neural networks (SNNs).
The Akida Development Environment includes the Akida Executiony 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.
‘It provides everything a user needs to develop, train, and run inference for spiking neural networks,’ says BrainChip.
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.
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.
The Akida Development Environment includes pre-created SNN models. Currently available models include a multi-layer perceptron implementation for MNIST in DVS format, a 7-layer network optimized for the CIFAR-10 dataset, and a 22-layer network optimized for the ImageNet dataset. These models can be the basis for users to modify, or to create their own custom SNN models.
The Akida Neuromorphic System-on-Chip NSoC is a spiking neural network acceleration device for production environments.
The Akida Development Environment includes the Akida Executiony 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.
‘It provides everything a user needs to develop, train, and run inference for spiking neural networks,’ says BrainChip.
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.
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.
The Akida Development Environment includes pre-created SNN models. Currently available models include a multi-layer perceptron implementation for MNIST in DVS format, a 7-layer network optimized for the CIFAR-10 dataset, and a 22-layer network optimized for the ImageNet dataset. These models can be the basis for users to modify, or to create their own custom SNN models.
The Akida Neuromorphic System-on-Chip NSoC is a spiking neural network acceleration device for production environments.
