Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollers
A new toolkit named Ariel-ML has been developed to automate parallelization for neural network inference on multi-core microcontrollers using embedded Rust. This toolkit is designed to leverage the capabilities of heterogeneous multi-core architectures found in various 32-bit microcontrollers, including Arm Cortex-M, RISC-V, and ESP-32 families. Benchmarks show that Ariel-ML achieves lower inference latency compared to existing solutions while maintaining comparable memory footprints to toolkits using C/C++. AI
IMPACT Enables more efficient AI model deployment on low-power, multi-core embedded systems.