Effortless Edge Deployment of AI Models with Digica’s AI SDK (feat. ExecuTorch)
1 week ago
Introduction
Deploying AI models on mobile and embedded devices is a challenge that goes far beyond just converting a trained model. While frameworks like PyTorch offer a streamlined way to develop deep learning models, efficiently deploying them on iOS, Android, and edge hardware requires significant engineering effort.
The problem isn’t just about performance - it’s about integration. How do you ensure that a model works reliably across different platforms? How do you optimize it for real-time execution on devices with limited computing power? How do you avoid the need for custom backend-specific workarounds?
While several AI runtimes, such as ONNX, are widely used across the industry, they often fail to fully utilize platform-specific optimizations. Many struggle to leverage the most efficient hardware acceleration available on devices, leading to suboptimal performance.
This is exactly where Digica’s AI SDK comes in. Built on Meta’s ExecuTorch, it eliminates the manual friction of model deployment by automating conversion, optimization, and integration for a wide range of mobile and embedded platforms. Whether you’re targeting Android, iOS, or any edge devices, the SDK ensures that your AI models are deployment-ready and utilize all the resources available with minimal effort.
The Challenge: Why Edge AI is Difficult
Running AI on edge devices – whether a smartphone, an embedded system, or an IoT device – is fundamentally different from deploying models in the cloud. There are three core challenges:
- Performance Constraints: Unlike cloud GPUs, mobile and embedded devices have limited processing power and memory. Running a large, unoptimized model can result in slow inference times and excessive power consumption, which are common deal-breakers for real-world applications.
- Hardware & Backend Fragmentation: AI models don’t “just work” across all platforms. Each hardware vendor has its own execution backend (CoreML for Apple devices, XNNPack for cross-platform, etc.), and each has different optimization and compatibility requirements.
- Complexity of Native Integration: Even if a model is successfully converted for a specific backend, it still needs to be integrated into a mobile or embedded application – often requiring manual C++ or Java development effort, which slows down deployment.
For companies developing AI-powered applications, these technical hurdles translate to longer development cycles, increased engineering costs, and a higher risk of compatibility issues. Digica’s AI SDK solves this by removing these obstacles, allowing developers to focus on their core application rather than the details of AI deployment.
The Solution: A Unified SDK for AI Model Deployment
The AI SDK was built to address these challenges by automating the entire process of AI model deployment. Instead of manually converting, optimizing, and integrating models for each platform, the SDK provides:
- Automated Model Conversion – PyTorch models are transformed into an optimized, deployable format that runs on CoreML MPS, XNNPack, QNN, and more – without requiring backend-specific engineering.
- Code Generation for Seamless Integration – The SDK automatically generates C++, Java, and Objective-C++ interfaces, eliminating the need for manual, low-level integration work.
- Cross-Platform Compatibility – Instead of maintaining separate deployment workflows for iOS, Android, and embedded devices, the SDK unifies them into a single streamlined process.
The result is faster time to market, reduced engineering overhead, and a fully optimized AI model that runs natively on mobile or edge devices.
A Real-World Example: Face Recognition on Mobile
To showcase the capabilities of our SDK, we developed a demo application using FRLib – a face recognition library that encapsulates several models for tasks such as detection, alignment, recognition, and emotion classification.
The demo application delivers real-time face recognition on mobile devices and achieves a total latency of only 20-25ms on an iPhone 15. This performance is reached with minimal effort during integration and even without any quantization techniques.
Why This Matters
AI deployment isn’t just about technical feasibility – it’s about time-to-market, cost efficiency, and scalability. Our SDK delivers tangible benefits:
- Faster AI Model Deployment – Cut engineering time from weeks to hours by automating tedious backend-specific optimizations.
- Cross-Platform Support – Deploy one model across multiple platforms without maintaining separate codebases.
- Cost Savings & Efficiency – Reduce dependency on cloud processing, leading to lower infrastructure costs and improved user experience.
Whether you're working on object detection, classification, maintenance, or any other AI-driven feature, the SDK ensures your models run efficiently where it matters – on the device itself.
Final Thoughts
Deploying AI models on mobile and embedded devices has historically been a complicated, fragmented process. Our SDK changes that by providing a unified, automated pipeline that simplifies conversion, optimization, and deployment. This allows teams to focus on innovation rather than infrastructure.
By leveraging ExecuTorch and our SDK, you can:
- Deploy AI faster
- Ensure cross-platform compatibility
- Reduce operational costs
The combination of automation, performance optimization, and seamless integration makes this an essential tool for anyone looking to bring AI to the edge.
If your team is working with AI on mobile or embedded platforms, our SDK might be the missing piece to help you scale faster. Contact us to learn more, or explore our demo to see the difference firsthand.