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 neutral platform to compare hardware and software stacks. The first public draft in 2020 outlined core components and a plugin interface. Since then, community releases have added back-end adapters, validation suites, and a benchmarking standard.

Architecture and components: The mlhav stack includes the WDL compiler, a cross-backend runtime, a set of benchmark kernels, and a back-end abstraction layer exposing performance counters, memory models, and power estimates. It emphasizes portability, reproducibility, and transparent reporting, with results comparable across platforms when similar workloads are used.

Usage and impact: In theory, mlhav provides researchers and practitioners with a neutral ground for evaluating hardware and software trade-offs. It is intended to complement vendor SDKs and comparative studies, guiding decisions about model deployment, compiler optimizations, and energy efficiency.

Limitations and reception: As an experimental tool, mlhav relies on accurate back-end implementations and careful methodology. Users should document workloads and environments to avoid non-comparable results. The project is released under a permissive open-source license.