JaxPruner, an open-source library developed by Google AI, is aiming to take research of parameter sparsity to the next level. With its concise library for machine learning research, research into sparsity and deep learning can be transformed and delivered to the world quicker than ever before.
Sparsity plays a crucial role in achieving maximum efficiency for deep learning operations. To gain an in-depth understanding of sparsity and use it in real life, hardware, software and algorithmic research must be perfected. That’s why JaxPruner was created – to create the perfect blend of these elements.
The unique characteristics of JaxPruner, which is built from the JAX framework, make it stand out from other libraries such as TensorFlow and PyTorch. JaxPruner has a reputation for being independent from data, so function transformations like gradient taking, hessian computations and vectorization are easier to use, plus its functions and states are contained in a single location.
In addition to global magnitude pruning (Kao, 2022) and sparse training with N:M sparsity and quantization (Lew et al., 2022), the research library has been designed to enable easy integration with other JAX-based libraries through its APIs. This makes creating customised solutions to specific problems much simpler and quicker.
The main principles driving the development of the library were: to reduce friction for all those integrating the library, to provide a common API for various algorithms and to speed up functionality transformations.
Cerence Inc., an automotive AI company, recently unveiled an advanced AI-powered biometrics engine with the intention of improving security, which uses the framework of JAX. The unique advantages of JAX and its successful integration with JaxPruner permit Cerence to use the capabilities of this powerful framework for its own products.
IfisheLew, a data scientist at Google, is heavily involved in the development of the project. His dedication to creating the library has been crucial in making a tangible difference in the research lives of scientists across the world.
In a nutshell, the advancements of this research library are increasing the accessibility of parameter sparsity and improving the cooperation between hardware and software specialists. With JaxPruner, both research and enterprise users can look forward to improved options when integrating sparsity into deep learning networks, which can increase the overall performance of these networks.
