Machine Learning Systems
On device streaming voice activity detection (Silero VAD v5) for Android. ~424 KB native binary, NEON-accelerated arm64-v8a, RTF ~3% on Snapdragon 662.
Production Android AI with ExecuTorch 1.0 - Deploy PyTorch models to mobile with NPU acceleration and 50KB footprint
Always-on wake-phrase detection for Android on the VoxRT custom on-device inference runtime — Kotlin library, 16 kHz mono PCM in, threshold-crossing events out. Custom phrases at voxrt.com.
LLM inference on mobile via Capacitor — run quantized GGUF models on-device
ONNX model execution on iOS and Android via Capacitor
Qualcomm® AI Hub Models is our collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.
📱 Optimized ML for edge devices. Showcasing efficient model deployment, GPU-CPU memory transfer optimization, and real-world edge AI applications. 🤖
Description: On-device Android AI assistant for face recognition, object detection, speech I/O, and memory-aware assistance using ExecuTorch, TFLite, Whisper, and Piper.
Android ML model server — download management, session caching, accelerator probing
Android ONNX runtime session management and preprocessing for Dust
Claude Code skill for Google LiteRT - on-device AI/ML deployment framework
On-device text embedding generation for iOS and Android via Capacitor
Model download and serving orchestration for Dust — Capacitor bridge
Largest list of models for Core ML (for iOS 11+)
Standalone model server business logic for iOS — download, caching, accelerator probing
Standalone ONNX runtime session management and preprocessing for Dust — iOS/macOS
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