TensorFlow Lite

Accelerating AI on Edge — Chintan Parikh and Weiyi Wang, Google DeepMind

May 5, 2026 · 23:58

Chintan Parikh and Weiyi Wang from Google DeepMind present Gemma 4E edge models (2B and 4B) and the LiteRT framework for on-device AI, arguing that edge AI delivers latency, privacy, offline capability, and cost savings. The Gemma 4E models introduce agentic capabilities including built-in function calling, structured JSON output, and chain-of-thought reasoning, all optimized for hardware-native support across CPUs, GPUs, and NPUs. LiteRT supports cross-platform deployment on Android, iOS, macOS, Linux, Windows, and IoT devices like Raspberry Pi, with a CLI tool and AI Edge portal for benchmarking. Performance benchmarks show up to 56 tokens per second on iOS, 30x speedups with NPU acceleration, and 35x faster than Llama on mobile. The Gallery app demonstrates on-device skills such as Wikipedia querying, mood tracking, photo-to-music generation, and voice agents, with open-source code and a Hugging Face repository. Q&A addresses use cases like local security camera face recognition, LiteRT vs TensorRT on Orin, multi-agent architectures, and audio model support.

Google Photos Magic Editor: GenAI Under the Hood of a Billion-User App - Kelvin Ma, Google Photos

Jul 19, 2025 · 20:28

Kelvin Ma, an engineer on Google Photos' editing team, explains how the billion-user app built the Magic Editor by integrating generative AI with on-device computational photography. He traces the evolution from earlier ML features like post-capture segmentation (UNet model, 10 MB) and Magic Eraser (a system of models) to the new server-side generative AI experience, which handles tasks like relocating objects and reimagining backgrounds. Key challenges include managing model size (now hundreds of MB), client-server latency, ambiguous problem scoping (e.g., moving from 5% to 80% reliability), and trust and safety. Ma advocates using evals, reducing ambiguity through product-research collaboration, and iterating from large general models to smaller, faster ones for production. He also highlights that Google Photos serves 1.5 billion monthly active users and processes hundreds of millions of edits per month, and the editor is being rebuilt as AI-first.