Forest Runtime provides an adaptable runtime environment for executing neural network models across various hardware platforms, including datacenters and mobile devices. Its modular architecture supports multiple modern AI applications, allowing for seamless integration with diverse hardware setups. By employing a retargetable approach, it facilitates model execution while minimizing synchronization overhead and maximizing hardware resource utilization.
The technology incorporates a feature called 'hot batching,' which enables the dynamic adjustment of model batch sizes and input shapes at runtime. This capability is pivotal in maintaining high throughput and minimal latency, especially within data center environments dealing with arbitrary changes in input configurations. Forest Runtime’s ability to fuse and streamline multiple model executions into a single process reduces CPU/NPU synchronization overhead, further optimizing processing efficiency.
Forest Runtime's scalable framework supports extensive application contexts, making it suitable for both single-user scenarios, such as smartphone apps, and multi-user environments typical in cloud services. Its integration capabilities extend to advanced neural network models like BERT and Transformer, featuring robust techniques to smoothly handle runtime fluctuations in input data characteristics.
