# WIA-SEMI-007: Neuromorphic Chip Standard

> **Brain-Inspired Computing for the Next Generation of AI**

[![Version](https://img.shields.io/badge/version-1.0.0-blue.svg)](https://github.com/WIA-Official/wia-standards)
[![License](https://img.shields.io/badge/license-CC%20BY%204.0-green.svg)](LICENSE)
[![Standard](https://img.shields.io/badge/standard-WIA--SEMI--007-purple.svg)](https://wia-standards.org)

## 🧠 Overview

WIA-SEMI-007 defines comprehensive standards for neuromorphic computing systems based on spiking neural networks (SNNs) and event-driven architectures. This standard enables:

- **10,000× Energy Efficiency** over traditional GPUs
- **Microsecond-scale Latency** for real-time processing
- **Brain-scale Computing** with billions of neurons
- **Event-driven Processing** eliminating idle power consumption

**Philosophy:** 홍익인간 (弘益人間) - Benefit All Humanity

## 📂 Repository Structure

```
neuromorphic-chip/
├── index.html              # Landing page with EN/KO toggle
├── simulator/              # 5-tab interactive simulator (99 languages)
│   └── index.html
├── ebook/                  # Comprehensive guides
│   ├── en/                 # 9 English chapters (145KB total)
│   │   ├── 00-cover.md
│   │   ├── 01-neuron-models.md
│   │   ├── 02-snn-architectures.md
│   │   ├── 03-memristor-hardware.md
│   │   ├── 04-encoding-protocols.md
│   │   ├── 05-tools-frameworks.md
│   │   ├── 06-benchmarks.md
│   │   ├── 07-deployment.md
│   │   └── 08-future-conclusion.md
│   └── ko/                 # 9 Korean chapters (119KB total)
│       └── ...
├── spec/                   # Technical specifications
│   ├── neuromorphic-chip-core-v1.0.md
│   ├── hardware-protocol-v1.0.md
│   ├── benchmarking-v1.0.md
│   └── safety-reliability-v1.0.md
├── api/                    # SDK implementations
│   └── typescript/         # TypeScript SDK
│       ├── package.json
│       ├── tsconfig.json
│       └── src/
│           ├── index.ts
│           └── types.ts
└── README.md               # This file
```

## 🚀 Quick Start

### 1. Explore the Landing Page

Open `index.html` in your browser to access:
- Interactive neuromorphic computing overview
- English/Korean language toggle
- 4-phase implementation guide
- Links to simulator and ebooks

### 2. Try the Simulator

Navigate to `simulator/index.html` for hands-on experience:
- **Tab 1:** 📊 Neuron/Synapse specifications
- **Tab 2:** 🔢 SNN calculations (network architecture, energy)
- **Tab 3:** 📡 Spike encoding (rate, latency, population, phase)
- **Tab 4:** 🔗 System integration (chip specs, power budget, memristor crossbar)
- **Tab 5:** 🧪 Benchmarking (N-MNIST, DVS-Gesture, SHD)

Supports **99 languages** via dropdown selector!

### 3. Read the E-books

Complete guides available in English and Korean:

**English (`ebook/en/`):**
1. Neuron Models & Synaptic Dynamics
2. SNN Architectures (feedforward, recurrent, convolutional)
3. Memristor Hardware & Crossbar Arrays
4. Spike Encoding Protocols
5. Development Tools & Frameworks
6. Benchmarks & Performance Metrics
7. Deployment Strategies
8. Future Directions

**Korean (`ebook/ko/`):**
- Full translations of all chapters
- Real Korean content (not machine-translated)

### 4. Use the TypeScript SDK

```bash
cd api/typescript
npm install
npm run build
```

**Example Usage:**
```typescript
import { LIFNeuron, SpikeEncoder, SpikingNeuralNetwork } from '@wia/neuromorphic-chip';

// Create LIF neuron
const neuron = new LIFNeuron();

// Simulate
const spiked = neuron.step(1.5, 0.1, 0);
console.log(`Neuron spiked: ${spiked}`);

// Encode input as spikes
const spikes = SpikeEncoder.rateEncode(0.75, 100, 200);
console.log(`Generated ${spikes.length} spikes`);

// Create SNN
const snn = new SpikingNeuralNetwork([784, 256, 10]);
const output = snn.forward(input_spikes, t);
```

## 📖 E-book Topics

### Core Concepts
- **Spiking Neural Networks vs Deep Neural Networks**
- **Event-driven Computing** (process only when spikes occur)
- **Temporal Coding** (information in spike timing)

### Hardware
- **Intel Loihi 2:** 1M neurons, 120M synapses, <100mW
- **IBM TrueNorth:** 1M neurons, 256M synapses, 70mW
- **BrainChip Akida:** Commercial edge AI, <1mW
- **Memristor Crossbars:** In-memory computing, 10× density

### Software Tools
- **Brian2:** Flexible Python-based simulator
- **SpikingJelly:** PyTorch integration with surrogate gradients
- **Lava:** Intel's framework for Loihi deployment
- **NEST:** Large-scale biological simulations

### Benchmarks
- **N-MNIST:** Neuromorphic MNIST (DVS recorded)
- **DVS-Gesture:** 11 hand gestures
- **SHD:** Spiking Heidelberg Digits (audio)
- **N-Caltech101:** 101 object categories

## 🔬 Technical Specifications

### Neuron Models

**LIF (Leaky Integrate-and-Fire):**
```
τ_m * dV/dt = -(V - V_rest) + R * I(t)

if V ≥ V_threshold:
    emit spike
    V ← V_reset
    enter refractory period
```

**Parameters:**
- τ_m: 20 ms (membrane time constant)
- V_rest: -70 mV (resting potential)
- V_threshold: -55 mV (spike threshold)
- V_reset: -70 mV (reset potential)

### Encoding Methods

| Method | Latency | Energy | Info/Spike | Use Case |
|--------|---------|--------|------------|----------|
| Rate | 50-200ms | High | 0.5-2 bits | Non-real-time |
| Latency | 1-10ms | Very Low | 4-8 bits | Real-time control |
| Population | 20-50ms | Medium | 2-4 bits | Robust motor control |
| Phase | 10-20ms | Low | 3-6 bits | Navigation |

### Performance Targets

**Energy Efficiency:**
- Minimum: 100 GOPS/W
- Target: 10 TOPS/W
- State-of-art: 100 TOPS/W

**Latency:**
- Rate coding: < 100ms
- Latency coding: < 10ms
- Real-time: < 1ms

**Accuracy:**
- N-MNIST: > 98%
- DVS-Gesture: > 95%
- SHD: > 90%

## 💻 Implementation Guide

### 1. Choose Hardware Platform

**Edge Devices:**
- Intel Loihi 2 USB Stick
- BrainChip Akida PCIe Card
- Microcontrollers (ARM Cortex-M)

**Cloud/Server:**
- TensorFlow Serving
- FastAPI + Docker
- Kubernetes deployment

### 2. Select Encoding Method

```python
# Rate coding (robust, high latency)
spikes = rate_encode(value, duration=100, max_rate=200)

# Latency coding (fast, low energy)
spike_time = latency_encode(value, time_window=20)

# Population coding (very robust)
firing_rates = population_encode(value, n_neurons=20)
```

### 3. Build Network

```python
from spikingjelly.clock_driven import neuron, layer

model = nn.Sequential(
    layer.Linear(784, 256),
    neuron.LIFNode(),
    layer.Linear(256, 10),
    neuron.LIFNode()
)
```

### 4. Train with Surrogate Gradients

```python
for epoch in range(10):
    for images, labels in train_loader:
        optimizer.zero_grad()

        # Forward pass
        output = model(images)
        loss = criterion(output, labels)

        # Backward pass (surrogate gradients)
        loss.backward()
        optimizer.step()

        # Reset neuron states
        functional.reset_net(model)
```

### 5. Deploy to Hardware

```python
# Convert to Loihi
from lava.magma.core.run_configs import Loihi2HwCfg

lava_model = convert_to_lava(model)
lava_model.run(condition=RunSteps(1000),
              run_cfg=Loihi2HwCfg())
```

## 🌍 Applications

### Edge AI
- Always-on keyword spotting (<100μW)
- Object detection in embedded systems
- Predictive maintenance
- Wearable health monitors

### Robotics
- Real-time sensorimotor control
- Adaptive learning without external training
- Energy-efficient mobile platforms

### Scientific Computing
- Drug discovery (protein folding)
- Climate modeling
- Materials science optimization

### Brain-Computer Interfaces
- Low-latency neural decoding
- Implantable devices
- Closed-loop neuromodulation

## 📊 Market Impact

**Energy Savings Potential:**
- If all AI inference switched to neuromorphic:
- Energy saved: 139 TWh/year
- Coal plants eliminated: ~28
- CO2 reduction: 60M tons/year

**Market Growth:**
- 2025: $750M (5% of edge AI)
- 2027: $5.25B (15% share)
- 2030: $25.5B (30% share)

## 🛠️ Development Roadmap

**Phase 1 (2025):** Foundation
- Core standards published ✅
- Reference implementations ✅
- Benchmark suite established ✅

**Phase 2 (2026-2027):** Adoption
- Industry partnerships
- Hardware vendor compliance
- Software ecosystem growth

**Phase 3 (2028-2030):** Maturity
- Widespread deployment
- 3D memristor integration
- Photonic neuromorphic chips

**Phase 4 (2031+):** Transformation
- Brain-scale systems (10B+ neurons)
- Quantum-neuromorphic hybrids
- AGI research applications

## 🤝 Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

**Ways to contribute:**
- Report bugs or issues
- Suggest new features
- Submit benchmark results
- Improve documentation
- Add language translations

## 📚 Resources

- **Landing Page:** [index.html](index.html)
- **Interactive Simulator:** [simulator/index.html](simulator/index.html)
- **E-book Store:** https://wiabooks.store/tag/wia-neuromorphic-chip/
- **GitHub:** https://github.com/WIA-Official/wia-standards
- **Community Forum:** https://forum.wia-standards.org

## 📄 License

**Documentation:** Creative Commons Attribution 4.0 International (CC BY 4.0)
**Code (SDK):** MIT License

## 🙏 Acknowledgments

Based on research from:
- Intel Labs (Loihi)
- IBM Research (TrueNorth)
- BrainChip (Akida)
- Universities worldwide

Special thanks to the neuromorphic computing community for advancing brain-inspired AI.

## 📞 Contact

- **Email:** standards@wia-official.org
- **Website:** https://wia-standards.org
- **Twitter:** @WIA_Official
- **Discord:** https://discord.gg/wia-standards

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© 2025 SmileStory Inc. / WIA-Official

**홍익인간 (弘益人間) · Benefit All Humanity**

*"The brain uses 20 watts to do what supercomputers need megawatts to achieve. Let's learn from three billion years of optimization."*

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**Keywords:** neuromorphic computing, spiking neural networks, SNN, brain-inspired AI, event-driven computing, memristor, crossbar array, Intel Loihi, IBM TrueNorth, BrainChip Akida, energy-efficient AI, edge AI, temporal coding, STDP, WIA-SEMI-007

**Version:** 1.0.0
**Status:** Stable
**Last Updated:** 2025-12-26

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**홍익인간 (弘益人間) - Benefit All Humanity** 🌍