ADE is a complete, industry-standard machine learning framework for creating, training and testing deeply learned neural networks. The platform leverages Tensorflow and Keras for neural network development, optimisation and training. Once the network model is fully trained, the ADE includes a simple-to-use compiler to map the network to the Akida fabric and run hardware accurate simulations on the Akida Execution Engine. The framework uses the Python scripting language and its associated tools and libraries, including Jupyter notebooks, Numpy and Matplotlib. With just a few lines, developers can easily run the Akida simulator on industry-standard datasets and benchmarks in the Akida model zoo such as Imagenet1000, Google Speech Commands, Mobilenet among others. Users can easily create, modify, train and test their own models within a simple use development environment.
“The enormous success of our early-adopters programme allowed us to make ADE available to developers looking to use an Akida-based environment for their deep machine learning needs,” said Louis DiNardo, CEO of Brainchip. “This is an important milestone for Brainchip as we continue to deliver our technology to a marketplace in search of a solution to overcome the power- and training-intense needs that deep learning networks currently require. With the ADE, designers can access the tools and resources needed to develop and deploy Edge application neural networks on the Akida neural processing technology.”
Akida is available as a licensable IP technology that can be integrated into ASIC devices and will be available as an integrated SoC, both suitable for applications such as surveillance, advanced driver assistance systems (ADAS), autonomous vehicles (AV), vision guided robotics, drones, augmented and virtual reality (AR/VR), acoustic analysis, and Industrial Internet-of-Things (IoT). Akida is a complete neural processing engine for edge applications, which eliminates CPU and memory overhead while delivering unprecedented efficiency, faster results, at minimum cost. Functions like training, learning, and inferencing are orders of magnitude more efficient with Akida.