Sold by: Aurora Labs
Deployed on AWS
The model provides detailed information about execution time for assembly code.
Overview
The model takes Tricore 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 Aurix Tricore TC399) 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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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.
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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 on the ml.g5.xlarge instance type. Both options use the same instance size, so pricing scales with how many host hours you run. The two dimensions differ by processing mode. Batch mode handles inference in grouped jobs. Real-time mode serves inference as requests arrive. Pick the mode that fits your workload, then multiply the hourly rate by hours used. There are no seats, tiers, or upfront commitments. Billing follows your actual usage.
Top-of-mind questions for buyers
You pay per host hour for a GPU-backed ml.g5.xlarge instance running the LOCI model. The model predicts time distribution for the Aurix Tricore ASM target. It reads compiled binaries and predicts timing behavior without running or simulating your code.
Charges accrue per host hour while the instance runs. Fully stopped instances do not accrue software host-hour charges. Underlying AWS storage or related resources may still bill separately. The software meters running time only, so you pay for hours the instance is active.
Both bill the same ml.g5.xlarge instance by host hour. Batch mode processes inference in grouped jobs, so you run hours only during job windows. Real-time mode keeps the instance available to serve requests as they arrive, so hours accrue across the serving window.
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 of LOCI LCLM for Time Distribution Prediction
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 TriCore 64-bit architecture. The code snippet below shows how to load the data in Python using the pandas package.
data = pandas.read_csv('../data/tricore_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/tricore_dataset.csv
https://github.com/auroralabs-loci/auroralabs_loci_marketplace/blob/main/data/tricore_dataset.csv
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