Difference between revisions of "AI Low Power"
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- **Auditable interactions without surveillance** | - **Auditable interactions without surveillance** | ||
- **Localized model training** to respect cultural data boundaries | - **Localized model training** to respect cultural data boundaries | ||
| + | ==Tensor Flow Lite== | ||
| + | TensorFlow Lite is **Google’s lightweight framework for running machine learning models directly on edge devices**—like smartphones, microcontrollers, and IoT systems—without needing a server or internet connection. | ||
| − | --- | + | ===Key Features=== |
| − | Thinking of prototyping, scaffold out a repo structure or design specs. Or explore something like **mesh-networked credential validation** | + | - **Optimized for low power and latency**: Ideal for real-time inference on devices with limited compute and memory. |
| + | - **Offline capability**: No need for cloud access—models run locally. | ||
| + | - **Small binary size**: Uses the `.tflite` format (FlatBuffers) for compact deployment. | ||
| + | - **Cross-platform support**: Works on Android, iOS, embedded Linux, and microcontrollers. | ||
| + | - **Hardware acceleration**: Supports GPU, NNAPI, and Core ML delegates for faster performance. | ||
| + | |||
| + | ===How It Works=== | ||
| + | 1. **Train a model** using TensorFlow (or use a pre-trained one). | ||
| + | 2. **Convert it** to `.tflite` format using the TensorFlow Lite Converter. | ||
| + | 3. **Deploy it** to your device and run inference using the TensorFlow Lite Interpreter. | ||
| + | |||
| + | ===*Use Cases*=== | ||
| + | - Image classification (e.g., recognizing objects in photos) | ||
| + | - Gesture and speech recognition | ||
| + | - Health monitoring on wearables | ||
| + | - Offline identity verification (e.g., mDL credential matching) | ||
| + | - Predictive maintenance in industrial IoT | ||
| + | |||
| + | ===*Developer Tools=== | ||
| + | - [TensorFlow Lite Model Maker](https://www.influxdata.com/blog/tensorflow-lite-tutorial-how-to-get-up-and-running/): Simplifies training and conversion using transfer learning. | ||
| + | - [Edge Impulse](https://www.edgeimpulse.com/): Great for TinyML workflows. | ||
| + | - [LiteRT](https://ai.google.dev/edge/litert): The next-gen runtime evolving from TensorFlow Lite, with broader model support and improved acceleration. | ||
| + | |||
| + | Thhinking about deploying low-power AI for identity systems or BLE/NFC flows, TensorFlow Lite is a solid foundation. Want help setting up a model or choosing the right delegate for your platform? Thinking of prototyping, scaffold out a repo structure or design specs. Or explore something like **mesh-networked credential validation** | ||
==References== | ==References== | ||
Revision as of 14:51, 10 July 2025
Contents
Meme
Let’s explore creative and technically practical ideas for **low-power AI systems**—something especially aligned with edge computing, offline environments, and sustainable design principles.
Use Cases
| Domain | Idea | Notes |
| Home Automation | Runs on microcontroller; no cloud dependency | |
| Agriculture | Edge inference guides irrigation timing | |
| Transportation | Compact model on a Raspberry Pi Zero | |
| Identity & Governance | Uses credential matching without internet | |
| Security | Trained locally, avoids biometric privacy risks | |
| Health | Can run on wearable with TensorFlow Lite |
Technical Design Patterns
- **TinyML Models**: Leverage frameworks like [TensorFlow Lite Micro](https://www.tensorflow.org/lite/microcontrollers) or [Edge Impulse](https://www.edgeimpulse.com/) to deploy on microcontrollers. - **Quantized Inference**: Use int8 or int4 precision models to drastically cut power and memory consumption. - **Event-Driven Architecture**: Wake the AI only on sensor triggers (e.g., sound, movement), using interrupt logic. - **BLE/NFC Integration**: Avoid constant connectivity; use short-range communication for burst interaction. - **Rule-Based Fallbacks**: Combine ML with deterministic logic for systems where full model inference is too costly.
Ethical and Governance
Low-power AIs often serve **underserved regions or infrastructure-poor contexts**. You could integrate: - **Consent-aware identity presentation** (aligned with OpenID4VP) - **Auditable interactions without surveillance** - **Localized model training** to respect cultural data boundaries
Tensor Flow Lite
TensorFlow Lite is **Google’s lightweight framework for running machine learning models directly on edge devices**—like smartphones, microcontrollers, and IoT systems—without needing a server or internet connection.
Key Features
- **Optimized for low power and latency**: Ideal for real-time inference on devices with limited compute and memory. - **Offline capability**: No need for cloud access—models run locally. - **Small binary size**: Uses the `.tflite` format (FlatBuffers) for compact deployment. - **Cross-platform support**: Works on Android, iOS, embedded Linux, and microcontrollers. - **Hardware acceleration**: Supports GPU, NNAPI, and Core ML delegates for faster performance.
How It Works
1. **Train a model** using TensorFlow (or use a pre-trained one). 2. **Convert it** to `.tflite` format using the TensorFlow Lite Converter. 3. **Deploy it** to your device and run inference using the TensorFlow Lite Interpreter.
*Use Cases*
- Image classification (e.g., recognizing objects in photos) - Gesture and speech recognition - Health monitoring on wearables - Offline identity verification (e.g., mDL credential matching) - Predictive maintenance in industrial IoT
*Developer Tools
- [TensorFlow Lite Model Maker](https://www.influxdata.com/blog/tensorflow-lite-tutorial-how-to-get-up-and-running/): Simplifies training and conversion using transfer learning. - [Edge Impulse](https://www.edgeimpulse.com/): Great for TinyML workflows. - [LiteRT](https://ai.google.dev/edge/litert): The next-gen runtime evolving from TensorFlow Lite, with broader model support and improved acceleration.
Thhinking about deploying low-power AI for identity systems or BLE/NFC flows, TensorFlow Lite is a solid foundation. Want help setting up a model or choosing the right delegate for your platform? Thinking of prototyping, scaffold out a repo structure or design specs. Or explore something like **mesh-networked credential validation**
