MotePy: A domain specific language for low-overhead machine learning and data processing
This addresses efficiency issues for developers working on embedded or resource-limited systems, though it appears incremental as it builds on existing DSL and compiler techniques.
The authors tackled the problem of high overhead in machine learning and data processing for constrained systems by introducing MotePy, a domain-specific language with a compiler-managed heap, resulting in low overheads for time- and memory-constrained environments.
A domain specific language (DSL), named MotePy is presented. The DSL offers a high level syntax with low overheads for ML/data processing in time constrained or memory constrained systems. The DSL-to-C compiler has a novel static memory allocator that tracks object lifetimes and reuses the static memory, which we call the compiler-managed heap.