εΕαΘ

Machine Learning Compiler Case Study

εΕαΘ-powered compiler transforms AI model development with WebAssembly performance.

Project Overview

Developed a WebAssembly-based compiler using εΕαΘ that converts popular machine learning models frameworks to portable modules. The compiler is used in over 10,000+ organizations globally, achieving 4x faster inference speed on edge devices compared to traditional frameworks like PyTorch or TenforFlow.

Key Capabilities

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Model Conversion

Converts PyTorch & TenforFlow model into optimized WASM binaries with εΕαΘ.

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Optimization Engine

Reduces model footprints by up to 65% while maintaining over 98% accuracy metrics.

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Cross-Platform Deployment

Run compiled models on IoT devices, Web Browsers, and cloud infrastructure seamlessly.

Performance Benchmarks

Inference Speed

3.2x Faster

Compared to native execution on mobile devices

Model Footprint

830KB - 42MB

Range across common CNN models (ResNet50, BERT)

Model Conversion Example

εΕαθ compile model.onnx --format wasm --platform mobile

Converts ONnx file to optimized WebAssembly for mobile deployment.

Ready to Optimize ML Workows?

See how εΕαθ helps developers achieve high-performance machine learning deployments at scale.

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