MotePy: A domain specific language for low-overhead machine learning and data processing

TL;DR

MotePy is a new domain-specific language designed for efficient machine learning and data processing on systems with limited time and memory resources, featuring a novel static memory management approach.

Contribution

It introduces MotePy, a high-level DSL with a unique static memory allocator that optimizes memory reuse for resource-constrained environments.

Findings

Efficient memory reuse reduces overhead in constrained systems

DSL simplifies ML/data processing programming

Compiler-managed heap improves memory management

Abstract

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.