2nd/10_Wiki/Topics/AI_and_ML/Edge-AI-and-Computing.md

---
id: wiki-2026-0508-edge-ai-and-computing
title: Edge AI and Computing
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [edge AI, on-device AI, edge computing, TinyML, NPU, mobile inference]
duplicate_of: none
source_trust_level: A
confidence_score: 0.95
verification_status: applied
tags: [ai, infrastructure, edge-computing, on-device-ai, latency, tinyml, quantization]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: C / C++ / Python
  framework: TFLite / ONNX Runtime / Core ML / NCNN / TinyML
---

# Edge AI and Computing

## 매 한 줄
> **"매 cloud 의 X — 매 device 의 inference"**. 매 latency ↓ + 매 privacy ↑ + 매 bandwidth ↓ + 매 offline. 매 model: 매 quantized + pruned + distilled. 매 hardware: 매 NPU (Apple Neural Engine, Snapdragon Hexagon), TinyML MCU. 매 modern: 매 on-device LLM (Phi-3, Llama 3.2 1B, Gemma 2B).

## 매 핵심

### 매 motivation
- **Latency**: 매 ms 의 round-trip cloud → 매 sub-ms.
- **Privacy**: 매 data 의 device 의 stay.
- **Cost**: 매 cloud GPU 의 X.
- **Offline**: 매 connectivity 의 independent.

### 매 model compression
- **Quantization**: FP32 → INT8 → INT4 → 4-bit.
- **Pruning**: 매 zero weights.
- **Distillation**: 매 teacher → student.
- **Architecture**: 매 MobileNet, EfficientNet, MobileViT.

### 매 hardware
- **Apple Neural Engine** (16-core).
- **Snapdragon Hexagon NPU**.
- **Google Tensor TPU edge**.
- **NVIDIA Jetson** (Orin, Xavier).
- **TinyML MCU**: ESP32, Cortex-M.
- **Coral Edge TPU**.

### 매 framework
- **Mobile**: TFLite, Core ML, MediaPipe, ONNX Runtime Mobile.
- **Embedded**: TensorFlow Lite Micro, Edge Impulse, NCNN, MNN.
- **LLM-on-device**: llama.cpp, MLC LLM, Apple Foundation Models, MLX.

### 매 응용
1. **Mobile photo**: 매 portrait, HDR.
2. **Voice**: 매 wake word, ASR.
3. **AR**: 매 hand tracking.
4. **IoT**: 매 anomaly.
5. **Automotive**: 매 ADAS.
6. **Wearable**: 매 ECG, sleep.

### 매 modern (2024+)
- **On-device LLM**: 매 Phi-3-mini (3.8B INT4), Llama 3.2 1B/3B, Gemma 2B.
- **Apple Foundation Models** framework.
- **Qualcomm AI Hub**.
- **Hybrid edge-cloud**: 매 simple → device, complex → cloud.

## 💻 패턴

### TFLite quantize (Python)
```python
import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_saved_model('saved_model/')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = lambda: ((tf.cast(x[:1], tf.float32),) for x in calibration_data)
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
open('model_int8.tflite', 'wb').write(tflite_model)
```

### Mobile inference (Core ML, Swift)
```swift
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .all  // 매 ANE + GPU + CPU
let model = try MyModel(configuration: config)
let prediction = try model.prediction(input: input)
```

### Android (TFLite + delegate)
```kotlin
val options = Interpreter.Options().apply {
    addDelegate(NnApiDelegate())  // 매 NPU
    setNumThreads(4)
}
val interpreter = Interpreter(modelFile, options)
interpreter.run(input, output)
```

### llama.cpp (LLM on-device)
```cpp
#include "llama.h"
struct llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = 99;  // 매 Metal / CUDA
struct llama_model* model = llama_load_model_from_file("model_q4.gguf", model_params);
struct llama_context_params ctx_params = llama_context_default_params();
struct llama_context* ctx = llama_new_context_with_model(model, ctx_params);
```

### MLX (Apple, Python)
```python
import mlx.core as mx
import mlx_lm

model, tokenizer = mlx_lm.load('mlx-community/Llama-3.2-1B-Instruct-4bit')
prompt = tokenizer.apply_chat_template([{'role': 'user', 'content': 'Hi'}], tokenize=False)
response = mlx_lm.generate(model, tokenizer, prompt, max_tokens=128)
```

### TinyML (TF Lite Micro, C)
```c
#include "tensorflow/lite/micro/all_ops_resolver.h"
constexpr int kArenaSize = 8 * 1024;
uint8_t tensor_arena[kArenaSize];

void setup() {
  static tflite::MicroErrorReporter error_reporter;
  static tflite::AllOpsResolver resolver;
  static tflite::MicroInterpreter interpreter(model, resolver, tensor_arena, kArenaSize);
  interpreter.AllocateTensors();
}

void loop() {
  TfLiteTensor* input = interpreter.input(0);
  // 매 fill from sensor
  interpreter.Invoke();
  TfLiteTensor* output = interpreter.output(0);
}
```

### Distillation (PyTorch)
```python
def distill_loss(student_logits, teacher_logits, target, T=3, alpha=0.5):
    soft = F.kl_div(F.log_softmax(student_logits / T, -1),
                    F.softmax(teacher_logits / T, -1), reduction='batchmean') * T * T
    hard = F.cross_entropy(student_logits, target)
    return alpha * soft + (1 - alpha) * hard
```

### Pruning (PyTorch)
```python
import torch.nn.utils.prune as prune
for module in model.modules():
    if isinstance(module, nn.Linear):
        prune.l1_unstructured(module, 'weight', amount=0.5)
        prune.remove(module, 'weight')
```

### Hybrid edge-cloud
```python
def smart_dispatch(query, device_capability):
    complexity = estimate_complexity(query)
    if complexity < THRESHOLD and device_capability.has_local_llm:
        return local_llm.generate(query)
    return cloud_llm.generate(query)
```

### Power-aware scheduling
```cpp
void schedule_inference() {
    if (battery_level < 0.2) {
        use_quantized_model();  // 매 INT8
        skip_low_priority_inferences();
    } else {
        use_full_model();
    }
}
```

### Latency benchmark
```python
import time
def benchmark_tflite(interpreter, input_data, n=100):
    times = []
    for _ in range(n):
        t0 = time.perf_counter()
        interpreter.set_tensor(input_idx, input_data)
        interpreter.invoke()
        times.append(time.perf_counter() - t0)
    return {'p50': sorted(times)[n // 2], 'p99': sorted(times)[int(n * 0.99)]}
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Mobile vision | TFLite + NNAPI / Core ML |
| Mobile LLM | MLX / llama.cpp / MLC |
| MCU (mW power) | TinyML / TF Lite Micro |
| Privacy critical | On-device only |
| Latency critical | Edge + 매 cache |
| Variable complexity | Hybrid edge-cloud |

**기본값**: 매 INT8 quantize + 매 NPU delegate + 매 device-tier model + 매 hybrid 의 fallback.

## 🔗 Graph
- 부모: [[Machine-Learning]] · [[Distributed-Systems]]
- 변형: [[TinyML]] · [[Mobile-AI]]
- 응용: [[LLM_Optimization_and_Deployment_Strategies|Quantization]] · [[LLM_Optimization_and_Deployment_Strategies|Knowledge-Distillation]]
- Adjacent: [[NPU]] · [[Federated-Learning]]

## 🤖 LLM 활용
**언제**: 매 mobile app. 매 IoT. 매 privacy. 매 low-latency.
**언제 X**: 매 huge model only. 매 frequent retrain.

## ❌ 안티패턴
- **Cloud model 의 device 의 push**: 매 ROM / RAM 의 fail.
- **No quantization**: 매 latency / battery.
- **Single delegate hardcode**: 매 device 의 fail.
- **Edge-only stubborn**: 매 hybrid 의 win 의 miss.
- **No power awareness**: 매 battery drain.

## 🧪 검증 / 중복
- Verified (TFLite docs, MLX, Apple WWDC, Qualcomm AI Hub).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-04-26 | EDGE-AI auto |
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — quantize + 매 TFLite / MLX / llama.cpp / TinyML / hybrid code |