Constructing an AI Compiler for ARM Cortex-M Devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code, which often requires specific treatment for each platform. The problem becomes critical on embedded devices, where computational and memory resources are strictly constrained. Compilers play an essential role in deploying source code on a target device through the backend. In this work, a novel backend for the Open Neural Network Compiler (ONNC) is proposed, which exploits machine learning to optimize code for the ARM Cortex-M device. The backend requires minimal changes to Open Neural Network Exchange (ONNX) models. Several novel optimization techniques are also incorporated in the backend, such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations. The performance of the proposed framework is evaluated for two applications: handwritten digit recognition on the Modified National Institute of Standards and Technology (MNIST) dataset and model, and image classification on the Canadian Institute For Advanced Research and 10 (CIFAR-10) dataset with the AlexNet-Light model. The system achieves 98.90% and 90.55% accuracy for handwritten digit recognition and image classification, respectively. Furthermore, the proposed architecture is significantly more lightweight than other state-of-the-art models in terms of both computation time and generated source code complexity. From the system perspective, this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it