Sold by: Aurora LabsÂ
Deployed on AWS
The model provides detailed information about execution time for assembly code.
Overview
The model takes ARM 64-bit assembly language blocks as input and provides a discrete probabilistic distribution for the execution time of the given block. The distribution can be used to sample mean execution time of the assembly code.
Highlights
- Use the model to optimize your code based on the generated assembly, leveraging learned performance patterns
- Predicts a probability distribution of execution time (in nanoseconds) for each block on the ARM Cortex-A53 core
- Extracts key timing metrics: Mean, Standard Deviation (STD), Max, and Median to understand performance variability at the ASM level
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $10.00 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $10.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial release for Amazon Sagemaker
Additional details
Inputs
- Summary
The input has to be a csv file, with one column named 'r.asm'. Each row can be assembly code based on the ARMv8 aarch64 architecture. The code snippet below shows how to load the data in Python using the pandas package.
data = pandas.read_csv('../data/arm_dataset.csv')
More detailed example is available in the Jupyter notebooks.
- Limitations for input type
- The csv file should have less than 2000 rows, for optimal performance. It is recommended that the length of one sample of assembly code is not longer than 500 instructions.
https://github.com/auroralabs-loci/auroralabs_loci_marketplace/blob/main/data/arm_dataset.csv
https://github.com/auroralabs-loci/auroralabs_loci_marketplace/blob/main/data/arm_dataset.csv
Support
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