Forest Runtime gives developers the flexibility to execute compiled neural networks across various hardware platforms, providing a seamless interface with its C++, C, and Python APIs. The system is designed with modular architecture that caters to datacenter, mobile, and TinyML applications, offering retargetability which simplifies integration across different hardware.
Forest Runtime stands out with its 'hot batching' technology, which allows for dynamic model partitioning, enhancing throughput and minimizing response times without runtime compilation transformations. It is engineered to be highly scalable with features allowing merging of models to reduce synchronization load between CPUs and NPUs, making it adaptable for data centers and mobile devices.
Its advanced design allows for flexibility in application scales and contexts, employing model fusion techniques and context switching to optimize resource use across systems with multiple accelerator cards. The setup exhibits robust scalability by leveraging software pipelining to enhance performance and manage memory allocation efficiently.
