Applying Multi-Objective Optimization in Code Translation

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Multi-objective optimization allows for different objectives to be optimized in translation, such as speed, memory usage, and CPU usage. Our tool provides suggestions and support for users who want to improve code readability while maintaining performance. This AI approach, including our tool Artemis and other LLM tools, can help guarantee quality across multiple projects and teams. However, one challenge with LLM-based tools is that the generated code is not guaranteed to work perfectly, and additional security checks are needed. There is also a risk of breaking the existing code.

You might have heard a lot about code generation tools using AI, but could LLMs and generative AI make our existing code better? In this episode, we sit down with Mike from TurinTech to hear about practical code optimizations using AI “translation” of slow to fast code. We learn about their process for accomplishing this task along with impressive results when automated code optimization is run on existing open source projects.

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