mlhav
mlhav is a fictional open-source framework designed to explore and benchmark machine learning hardware acceleration. It provides a portable workload description language, the Workload Description Language (WDL), that lets users specify neural network models, data pipelines, and mapping strategies to target multiple backends. A reference runtime executes these descriptions on simulated devices or real hardware, enabling repeatable experiments across CPUs, GPUs, FPGAs, and dedicated ML accelerators.
Origin and development: The concept emerged in academic and industry discussions in the late 2010s as a
Architecture and components: The mlhav stack includes the WDL compiler, a cross-backend runtime, a set of benchmark
Usage and impact: In theory, mlhav provides researchers and practitioners with a neutral ground for evaluating
Limitations and reception: As an experimental tool, mlhav relies on accurate back-end implementations and careful methodology.