The Neural ASIC Design Framework is a sophisticated platform designed to facilitate the development of Neural Processing Unit or Neuromorphic Processor Unit (NPU) Intellectual Property (IP). This framework leverages the Neural IP Portfolio, which includes a wide range of components specifically designed to support the development of Artificial Intelligence (AI) Systems on Chip (SoCs).
One of the key features of this framework is its support for a 1K Configurable Convolutional Neural Network (CNN) Engine. This engine is capable of conducting matrix multiplications with highly utilized computing units, regardless of the access patterns, shapes, and dimensions of the input matrices. This makes it highly flexible and adaptable to various neural network architectures.
Moreover, the framework has the potential for cascading to support 4K, thereby enhancing its scalability and performance. This feature allows for the efficient processing of larger and more complex neural networks, making it suitable for a wide range of AI applications.
The Neural ASIC Design Framework is also compatible with popular deep learning frameworks such as TensorFlow, Caffe, and Keras. This compatibility ensures that developers can seamlessly integrate their existing models developed in these frameworks into the ASIC design process.
Furthermore, the framework supports popular CNN models such as AlexNet, ResNet, GoogleNet, and YOLO. These models have been widely used in various AI applications, and their support ensures that developers can leverage their proven capabilities in their ASIC designs.
In summary, the Neural ASIC Design Framework provides a comprehensive and flexible platform for designing NPU IPs. Its support for a wide range of neural network components, compatibility with popular deep learning frameworks, and ability to handle popular CNN models make it a powerful tool for developing high-performance, efficient, and scalable AI systems.