Forest Runtime provides a sophisticated platform for executing compiled neural network models, supporting a variety of AI applications across multiple hardware configurations. Its unique C++ API, with bindings in C and Python, allows seamless integration into diverse systems ranging from data centers to mobile and TinyML devices. With its retargetable design, Forest Runtime ensures compatibility with various platforms, dynamically adjusting to the demands of modern neural network architectures.
One defining feature of Forest Runtime is its ability to support "hot batching," a technique that enables runtime changes in model batch sizes and input shapes without invoking compilation transformations. This feature is particularly advantageous in data centers, enhancing throughput by optimizing hardware utilization and minimizing response times.
Moreover, Forest Runtime scales effectively with technologies like model fusion and context switching, facilitating the management of multiple neural network models and tasks. Its use of modern linker technology to bridge tasks across accelerator cards further enhances system efficiency, ensuring comprehensive platform utilization.
