Sold by: Aurora Labs
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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Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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 |
Dimensions summary
You pay by the hour for model inference running on the ml.g5.xlarge instance type. Billing is usage-based, so charges track the hours the instance runs. The two dimensions split by inference mode. Batch mode processes grouped requests together. Real-time mode handles requests as they arrive. Both use the same instance type, so your choice depends on how you send workloads, not on instance size. You can run either mode, and costs scale with host hours consumed.
Top-of-mind questions for buyers
You run inference for a code-language model that predicts software execution behavior. It reads compiled binaries and predicts per-function timing, energy cost, throughput, and stack pressure ahead of runtime. The model covers ARMv8 AArch64 assembly. Billing tracks the instance hours consumed while inference runs.
Both modes meter host hours on the same ml.g5.xlarge instance. Batch mode groups multiple requests and processes them together, suiting large offline prediction jobs. Real-time mode handles requests one at a time as they arrive, suiting interactive use. Cost tracks the hours each mode runs, not the number of requests.
Software charges apply per host hour while the instance runs. A fully stopped instance does not accrue software charges. Stopped instances may still incur underlying AWS storage fees, but the software license meters running time only. To stop billing, shut down the instance when inference is not needed.
www.auroralabs.com
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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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