Sold by: Aurora LabsÂ
Deployed on AWS
LOCI Large Code Language Model (LCLM) for Time Prediction on ARMv8 AArch64 ASM
The model takes ARM 64-bit assembly language blocks as input and provides cycle time and its standard deviation for the given block
Overview
The model takes ARMv8 AArch64 assembly blocks as input and predicts execution time and standard deviation for each block on the Cortex-A53 core.
Highlights
- Predicts mean execution time (in nanoseconds) on the ARM Cortex-A53 core
- Estimates standard deviation to capture timing variability
- Supports timing-aware code optimization and performance tuning
Details
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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 |
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Delivery details
Amazon SageMaker model
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Version release notes
Initial ML model release for Amazon SageMaker through AWS Marketplace
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
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