TVM is a machine learning compiler framework designed to convert deep learning models from various frameworks into optimized machine code. It functions as a cross-platform deployment engine that transforms high-level model definitions into efficient, hardware-specific binaries for diverse computing architectures.
The system utilizes a multi-level compilation pipeline that decouples algorithm logic from hardware implementation through tensor-operator abstractions. It employs a graph-level intermediate representation to perform cross-operator optimizations and memory planning before lowering computations to target-specific instructions. To maximize performance, the framework includes an automated schedule space search that explores potential loop transformations and hardware mappings, alongside a lightweight virtual machine runtime for consistent model execution.
This toolkit supports the deployment of computational workloads across a wide range of devices, including CPUs, GPUs, and specialized accelerators. It provides capabilities for cross-compiling models for various operating systems and processor architectures, facilitating the development of high-performance machine learning applications for resource-constrained edge devices.
