Difference between revisions of "AI Low Power"
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# Try to find a new paradigm for [[Artificial Intelligence]] that applies to the cloud based servers as well as smaller sites. | # Try to find a new paradigm for [[Artificial Intelligence]] that applies to the cloud based servers as well as smaller sites. | ||
| − | ===Use Cases=== | + | ===Low Power Use Cases=== |
{| border="1" padding="2" width="888px" | {| border="1" padding="2" width="888px" | ||
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===Technical Design Patterns=== | ===Technical Design Patterns=== | ||
| − | + | *TinyML Models**: Leverage frameworks like [https://www.tensorflow.org/lite/microcontrollers TensorFlow Lite Micro] or [https://www.edgeimpulse.com/ Edge Impulse] to deploy on micro-controllers. | |
| − | + | *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. | |
| + | |||
| + | ===Samsung Tiny RM=== | ||
| + | Samsung’s Tiny Recursive Model (TRM) is a stunning rebuttal to the “bigger is better” orthodoxy in AI. The paper titled “Less is More: Recursive Reasoning with Tiny Networks” introduces a 7-million-parameter model that outperforms much larger systems on reasoning tasks like Sudoku, mazes, and abstract AGI benchmarks2. | ||
| + | * https://github.com/SamsungSAILMontreal/TinyRecursiveModels | ||
| + | * https://siliconangle.com/2025/10/09/samsung-researchers-created-tiny-ai-model-shames-biggest-llms-reasoning-puzzles/ | ||
| + | ====Core Innovation: Recursive Reasoning==== | ||
| + | Instead of stacking layers or scaling parameters, TRM uses recursive loops to refine its answers: | ||
| + | |||
| + | Drafts an initial solution | ||
| + | |||
| + | Self-critiques using a latent scratchpad | ||
| + | |||
| + | Iterates up to 16 times, improving with each pass | ||
| + | |||
| + | Halts adaptively when the answer stabilizes | ||
| + | |||
| + | This mimics human reasoning: “Is my answer good? Can I make it better?”—without requiring massive compute. | ||
===Ethical and Governance=== | ===Ethical and Governance=== | ||
Low-power AIs often serve **underserved regions or infrastructure-poor contexts**. You could integrate: | 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== | ==Tensor Flow Lite== | ||
| Line 44: | Line 61: | ||
===Key Features=== | ===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=== | ===How It Works=== | ||
| − | + | # **Train a model** using TensorFlow (or use a pre-trained one). | |
| − | + | # **Convert it** to `.tflite` format using the TensorFlow Lite Converter. | |
| − | + | # **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. | |
| − | + | Thinking 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 Network]] credential validation** | |
| − | == | + | ==New Low Power Paradigm== |
| + | ===IBM’s Analog AI Chips=== | ||
| + | IBM researchers have built analog AI chips that perform natural-language tasks with up to 14× greater energy efficiency than digital counterparts. These chips use phase-change memory to store neural weights directly on the chip, reducing data movement and power consumption. They’ve demonstrated real-time speech recognition and transcription with impressive accuracy. | ||
| + | |||
| + | Explore more in IBM’s research blog. | ||
| + | ===Spiking=== | ||
| + | SpikingBrain1.0 is an important development, but the headlines need context. Spiking neural networks can be more efficient than transformers, especially on very long sequences where quadratic complexity bogs models down. In those scenarios, speedups of “100×” are possible. But that doesn’t mean this replaces today’s LLMs, for most real-world tasks, the gains are smaller, and the tradeoffs in accuracy and robustness aren’t clear. | ||
| + | |||
| + | The strategic angle matters even more. By proving this on domestic hardware, China reduces its dependency on U.S. chips. That aligns neatly with Beijing’s plan to scale AI as a pillar of national power. Efficiency isn’t just about lower costs, it’s about enabling deployment at massive scale, outside Western control. | ||
| + | The security community should pay close attention. Faster, cheaper models with longer context windows don’t just improve productivity, they also make AI-driven Data Attacks (AIDA) more effective, from mining metadata to correlating patterns over time. Gains in speed can accelerate adversaries as easily as innovators. | ||
| + | SpikingBrain may be a milestone. Now it's whether we evolve our defenses, inference-resistant cryptography, verifiable provenance, before these breakthroughs are turned against us. | ||
| + | |||
| + | ===Spiking Neural Networks (SNNs) from IIT Bombay=== | ||
| + | Researchers developed a chip using band-to-band tunneling (BTBT) to power spiking neural networks. These mimic brain-like behavior and consume 5,000× less energy per spike than conventional designs. Ideal for speech recognition, motion detection, and biomedical signals on edge devices. | ||
| + | |||
| + | Read the IEEE Spectrum article [https://spectrum.ieee.org/low-power-ai-spiking-neural-net here.] | ||
| + | |||
| + | ===Falcons.AI + Intel Liftoff=== | ||
| + | Falcons.AI created a 4MB neural network that uses on/off pulse logic inspired by biological neurons. It consumes 10× less energy than CNNs and 100× less than vision transformers, making it perfect for wearables, wildlife cameras, and smart agriculture. | ||
| + | |||
| + | Details available on Intel’s blog. | ||
| + | |||
| + | ===Analog Devices’ AI Microcontrollers=== | ||
| + | These ultra-low-power MCUs come with built-in neural network accelerators, enabling real-time audio and image inference on devices like thermostats and smartwatches. They eliminate the need for cloud processing, saving energy and enhancing privacy. | ||
| + | |||
| + | Check out the product line here. | ||
| + | |||
| + | ===AIoT Chip Research from Peking University=== | ||
| + | A comprehensive study outlines architectural strategies for AIoT chips that perform intelligent inference directly on sensor nodes. Techniques include adaptive ADCs, TinyML, and custom analog front ends to reduce power draw while maintaining performance. | ||
| + | |||
| + | You can read the full research paper [https://link.springer.com/article/10.1007/s11432-023-3813-8 here]. | ||
==References== | ==References== | ||
[[Category: Artificial Intelligence]] | [[Category: Artificial Intelligence]] | ||
| + | [[Category: Networking]] | ||
Latest revision as of 20:37, 9 October 2025
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.
Context
Artificial Intelligence Consumes much more power than, for example, the human brain, which runs on 20 watts. There are two ways to reduce power:
- Just be low power systems for smaller jobs, or
- Try to find a new paradigm for Artificial Intelligence that applies to the cloud based servers as well as smaller sites.
Low Power 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 or Edge Impulse to deploy on micro-controllers.
- 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.
Samsung Tiny RM
Samsung’s Tiny Recursive Model (TRM) is a stunning rebuttal to the “bigger is better” orthodoxy in AI. The paper titled “Less is More: Recursive Reasoning with Tiny Networks” introduces a 7-million-parameter model that outperforms much larger systems on reasoning tasks like Sudoku, mazes, and abstract AGI benchmarks2.
- https://github.com/SamsungSAILMontreal/TinyRecursiveModels
- https://siliconangle.com/2025/10/09/samsung-researchers-created-tiny-ai-model-shames-biggest-llms-reasoning-puzzles/
Core Innovation: Recursive Reasoning
Instead of stacking layers or scaling parameters, TRM uses recursive loops to refine its answers:
Drafts an initial solution
Self-critiques using a latent scratchpad
Iterates up to 16 times, improving with each pass
Halts adaptively when the answer stabilizes
This mimics human reasoning: “Is my answer good? Can I make it better?”—without requiring massive compute.
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
- **Train a model** using TensorFlow (or use a pre-trained one).
- **Convert it** to `.tflite` format using the TensorFlow Lite Converter.
- **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.
Thinking 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 Network credential validation**
New Low Power Paradigm
IBM’s Analog AI Chips
IBM researchers have built analog AI chips that perform natural-language tasks with up to 14× greater energy efficiency than digital counterparts. These chips use phase-change memory to store neural weights directly on the chip, reducing data movement and power consumption. They’ve demonstrated real-time speech recognition and transcription with impressive accuracy.
Explore more in IBM’s research blog.
Spiking
SpikingBrain1.0 is an important development, but the headlines need context. Spiking neural networks can be more efficient than transformers, especially on very long sequences where quadratic complexity bogs models down. In those scenarios, speedups of “100×” are possible. But that doesn’t mean this replaces today’s LLMs, for most real-world tasks, the gains are smaller, and the tradeoffs in accuracy and robustness aren’t clear.
The strategic angle matters even more. By proving this on domestic hardware, China reduces its dependency on U.S. chips. That aligns neatly with Beijing’s plan to scale AI as a pillar of national power. Efficiency isn’t just about lower costs, it’s about enabling deployment at massive scale, outside Western control. The security community should pay close attention. Faster, cheaper models with longer context windows don’t just improve productivity, they also make AI-driven Data Attacks (AIDA) more effective, from mining metadata to correlating patterns over time. Gains in speed can accelerate adversaries as easily as innovators. SpikingBrain may be a milestone. Now it's whether we evolve our defenses, inference-resistant cryptography, verifiable provenance, before these breakthroughs are turned against us.
Spiking Neural Networks (SNNs) from IIT Bombay
Researchers developed a chip using band-to-band tunneling (BTBT) to power spiking neural networks. These mimic brain-like behavior and consume 5,000× less energy per spike than conventional designs. Ideal for speech recognition, motion detection, and biomedical signals on edge devices.
Read the IEEE Spectrum article here.
Falcons.AI + Intel Liftoff
Falcons.AI created a 4MB neural network that uses on/off pulse logic inspired by biological neurons. It consumes 10× less energy than CNNs and 100× less than vision transformers, making it perfect for wearables, wildlife cameras, and smart agriculture.
Details available on Intel’s blog.
Analog Devices’ AI Microcontrollers
These ultra-low-power MCUs come with built-in neural network accelerators, enabling real-time audio and image inference on devices like thermostats and smartwatches. They eliminate the need for cloud processing, saving energy and enhancing privacy.
Check out the product line here.
AIoT Chip Research from Peking University
A comprehensive study outlines architectural strategies for AIoT chips that perform intelligent inference directly on sensor nodes. Techniques include adaptive ADCs, TinyML, and custom analog front ends to reduce power draw while maintaining performance.
You can read the full research paper here.
