The MERA Compiler and Software Framework provides a comprehensive platform for deploying neural network models across various systems. Designed with a framework-agnostic approach, MERA allows developers to leverage predefined models from leading libraries such as Hugging Face, facilitating straightforward AI model deployment and integration without needing to dive into chip-level intricacies.
Key to MERA's utility is its ability to optimize the deployment of AI inference by enabling deep neural network graph compilation via the Dynamic Neural Accelerator (DNA) architecture. MERA simplifies the process of deploying pre-trained neural networks by handling all aspects, from APIs, code generation, to runtime needs. It is especially adept at managing generative AI applications, giving users the capacity to generate novel content in fields like vision, language, and audio.
MERA is compatible with an array of processing architectures, including AMD, Intel, Arm, and RISC-V. This ensures broad applicability and integration into existing infrastructures. Furthermore, it includes native support for popular machine learning frameworks like TensorFlow Lite and ONNX, making it a flexible solution for software developers and data scientists. Its open-source elements allow for easy distribution and collaboration across project teams, enhancing workflow integration and reducing the time-to-market for AI solutions.
