Software compresses neural networks without loss of accuracy
The Fraunhofer Heinrich Hertz Institute (HHI) has developed NNCodec, a new software that compresses neural networks to a fraction of their size without loss of accuracy. For non-commercial use only, NNCodec is now available as a free download via the software development platform GitHub. NNCodec specifically addresses research groups and development teams in the field of AI.
NNCodec is an open-source, user-friendly software that includes encoder and decoder modules for compressing neural networks. This software implements the new MPEG standard “Neural Network Coding (NNC — ISO/IEC 15938-17:2022)”, co-developed by Fraunhofer HHI and formally approved by ISO/IEC in August 2022.
Neural networks are the backbone in most AI technologies and have become essential for many applications. As a result, different neural network architectures have emerged for various use cases. Simultaneously, these networks are becoming increasingly complex, i.e. the number of layers, links and parameters has increased dramatically. Consequently, such networks require fast-growing computing power and memory. Without efficient compression, neural networks can hardly be integrated into mobile phones. At the same time, complex neural networks require a high data transmission rate.
NNCodec compresses trained neural networks to 5 to 10 percent of their original size, while maintaining their inference accuracy. This allows neural networks to be efficiently stored and transferred to other AI applications. Among the many scenarios that benefit from using the NNC standard are 5G applications, image and video compression, 3D reconstruction and coding methods, as well as AI technologies for mobility, such as autonomous driving. Especially in distributed learning environments such as federated learning, neural network updates need to be sent regularly between participating devices. Here, the NNC standard enables a significant reduction in the required communication bit rate.
“NNCodec, an open-source and user-friendly software realization of NNC, just a few months after the formal approval of the standard,” says Dr. Detlev Marpe, head of the department “Video Communication and Applications” at Fraunhofer HHI.
Professor Wojciech Samek, head of the “Artificial Intelligence” department at Fraunhofer HHI further comments: “We are happy to serve a wide range of applications for the latest AI developments and neural networks, such as CNNs, auto-encoders or transformer networks with this optimized software.”
The NNCodec software with encoder and decoder modules is available on GitHub.
Further information is available here.
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