Sold by: Aurora Labs
Deployed on AWS
LOCI LCLM for Time prediction on TriCore 64-bit assembly code.
Overview
The model takes TriCore 64-bit assembly blocks as input and predicts execution time and standard deviation for each block on the Infineon TC399 core.
Highlights
- Predicts mean execution time in nanoseconds on the Tricore TC399 core
- Provides standard deviation to assess runtime variability
- Enables code-level optimization and timing-aware decision making during development
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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 | $5.00 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $5.00 |
Dimensions summary
You pay by the hour for model inference, based on how you run it. Both options use the ml.g5.xlarge instance type and bill per host hour used. The difference is the inference mode. Batch mode processes grouped requests together, while real-time mode handles requests as they arrive. You choose the mode that fits your workload and pay only for the hours each instance runs. There is no upfront commitment; charges scale with your usage.
Top-of-mind questions for buyers
You run a code-language model that predicts how compiled software will behave on Aurix TriCore hardware. It reads the binary and estimates timing, energy, and memory before code runs. The ml.g5.xlarge instance is a GPU-backed AWS instance type that hosts this model for inference.
Both bill per host hour on the same instance type. Batch mode groups requests and processes them together, which suits large one-time prediction jobs. Real-time mode responds to requests as they arrive, which suits interactive use. You pick the mode that matches your workload and pay for the hours each runs.
Charges accrue per host hour while the instance runs. When you stop the instance, software host-hour charges stop. Underlying AWS resource fees, such as storage, may still apply while the instance is stopped. The software meters running time only.
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 prediction for TriCore 64-bit assembly.
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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