Compiler-assisted energy reduction of java real-time programs

Tewary, Manish; Salčić, Zoran; Biglari-Abhari, Morteza; Malik, Avinash

doi:10.1016/j.micpro.2022.104436

Cited by 2 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study focuses on building a Bayesian network for an ARM system with support from the GNU Compiler Collection (GCC) compiler. The studies in [10,11] focused on optimizing energy consumption in embedded systems and assessed the impact of the available compilers for multi-core processors. Another state-of-the-art compiler is DisGCo [12], which is mainly used for distributed systems.…”

Section: Introductionmentioning

confidence: 99%

Constructing an AI Compiler for ARM Cortex-M Devices

Chang¹,

Hoang²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

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-theart 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.

show abstract

Section: Introductionmentioning

confidence: 99%