Edge AI Management Protocol
Core Responsibilities
1. Model Management Strategy
Lazy Loading Implementation:
# Model loader with on-demand initialization
class LazyModelLoader:
def __init__(self, model_paths: Dict[str, str]):
self.loaded_models = {}
self.model_paths = model_paths
self.memory_threshold = 0.8 # 80% memory usage threshold
def load_model(self, model_name: str) -> Optional[Any]:
"""Load model only when needed"""
if model_name not in self.loaded_models:
if self._check_memory_pressure():
self._unload_least_recently_used()
self.loaded_models[model_name] = self._load_from_disk(model_name)
return self.loaded_models[model_name]
Memory Pressure Detection:
- Monitor RAM usage via
psutil - Trigger unload when memory > 80%
- LRU (Least Recently Used) eviction strategy
- Preload frequently used models during idle time
2. Quantization Strategy
Dynamic Quantization Based on Device Capabilities:
class QuantizationManager:
def __init__(self):
self.device_capabilities = self._detect_device_capabilities()
def _detect_device_capabilities(self) -> Dict[str, Any]:
return {
'ram_gb': psutil.virtual_memory().total / (1024**3),
'cpu_cores': psutil.cpu_count(),
'device_age': self._estimate_device_age(),
'gpu_available': torch.cuda.is_available()
}
def get_quantization_config(self, model_size: str) -> str:
"""Return optimal quantization based on device"""
ram = self.device_capabilities['ram_gb']
if ram < 4:
return "int4" # Aggressive quantization for older devices
elif ram < 8:
return "int8" # Balanced for mid-range devices
else:
return "fp16" # Minimal quantization for high-end devices
Quantization Levels:
- int4: 4-bit quantization for devices < 4GB RAM (pre-2020)
- int8: 8-bit quantization for devices 4-8GB RAM
- fp16: 16-bit floating point for devices > 8GB RAM
3. Context Window Management
Sliding Window with Semantic Chunking:
class ContextWindowManager:
def __init__(self, max_tokens: int = 4096):
self.max_tokens = max_tokens
self.current_context = []
self.embedding_cache = {}
def add_to_context(self, text: str) -> None:
"""Add text with smart context management"""
tokens = self._tokenize(text)
if len(tokens) > self.max_tokens:
# Use semantic chunking to preserve relevant context
chunks = self._semantic_chunk(tokens)
self.current_context.extend(chunks)
self._prune_context()
else:
self.current_context.append(text)
self._prune_context()
def _semantic_chunking(self, tokens: List[str]) -> List[str]:
"""Chunk preserving semantic coherence"""
# Use embedding similarity to group related content
embeddings = self._compute_embeddings(tokens)
# Group by similarity threshold
# Keep most recent + most relevant chunks
return self._select_optimal_chunks(embeddings)
4. Battery Optimization
Batch Inference and Throttling:
class BatteryOptimizer:
def __init__(self):
self.battery = psutil.sensors_battery()
self.batch_queue = []
self.batch_size = 10
self.low_battery_mode = False
def should_batch_inference(self) -> bool:
"""Determine if batching is beneficial"""
if self.battery.percent < 20:
self.low_battery_mode = True
return True # Always batch in low battery
return len(self.batch_queue) >= self.batch_size
def add_inference_request(self, request: Dict) -> None:
"""Queue request for batch processing"""
self.batch_queue.append(request)
if self.should_batch_inference():
self._process_batch()
def _process_batch(self) -> None:
"""Process queued requests in batch"""
if not self.batch_queue:
return
# Single inference for entire batch
batch_input = self._combine_batch_inputs(self.batch_queue)
results = self._run_inference(batch_input)
# Distribute results
self._distribute_results(results)
self.batch_queue.clear()
5. Model Selection Algorithm
Runtime Model Selection:
class ModelSelector:
def __init__(self):
self.available_models = {
'small': {'size_mb': 100, 'quality': 0.7, 'speed': 0.9},
'medium': {'size_mb': 500, 'quality': 0.85, 'speed': 0.7},
'large': {'size_mb': 2000, 'quality': 0.95, 'speed': 0.4}
}
def select_model(self, task_type: str, constraints: Dict) -> str:
"""Select optimal model based on task and constraints"""
available_ram = constraints.get('available_ram_gb', 4)
battery_percent = constraints.get('battery_percent', 100)
priority = constraints.get('priority', 'balanced') # speed, quality, balanced
# Filter models that fit in memory
feasible_models = [
name for name, specs in self.available_models.items()
if specs['size_mb'] < (available_ram * 1024 * 0.6) # Use max 60% of RAM
]
if not feasible_models:
return 'small' # Fallback to smallest model
# Score models based on priority
scored_models = []
for model in feasible_models:
specs = self.available_models[model]
score = self._calculate_score(specs, priority, battery_percent)
scored_models.append((model, score))
# Return highest scoring model
return max(scored_models, key=lambda x: x[1])[0]
def _calculate_score(self, specs: Dict, priority: str, battery: int) -> float:
"""Calculate model suitability score"""
if priority == 'speed':
return specs['speed'] * (1 if battery > 30 else 0.5)
elif priority == 'quality':
return specs['quality'] * (1 if battery > 20 else 0.3)
else: # balanced
return (specs['speed'] + specs['quality']) / 2 * (1 if battery > 25 else 0.4)
Implementation Patterns
Pattern 1: Resource-Aware Model Loading
# Example usage
manager = EdgeAIManager()
# Load model based on current resources
model = manager.load_model(
model_name="text_generator",
constraints={
'max_memory_mb': 512,
'battery_percent': 45,
'priority': 'balanced'
}
)
# Model automatically quantized and optimized
output = model.generate(input_text)
Pattern 2: Context-Aware Processing
# Context window automatically manages memory
context_manager = ContextWindowManager(max_tokens=2048)
for document in documents:
context_manager.add_to_context(document)
# Oldest/least relevant context automatically pruned
Pattern 3: Battery-Smart Inference
# Inference automatically optimized for battery
optimizer = BatteryOptimizer()
# Requests automatically batched in low battery
optimizer.add_inference_request(request1)
optimizer.add_inference_request(request2)
# Processed together when batch threshold reached or battery low
Integration with CatToolkit
Usage in Builder Workflow:
# Use edge-ai-management skill for mobile optimization
"Optimize this model for edge deployment on mobile devices"
# Skill automatically:
# 1. Detects device capabilities
# 2. Applies appropriate quantization
# 3. Configures memory management
# 4. Implements battery optimization
# 5. Generates deployment configuration
Quality Gates
- Model size after quantization < 60% of available RAM
- Battery impact < 10% per hour of active use
- Context window maintains semantic coherence
- Memory pressure never exceeds 80%
- Cold start time < 3 seconds for cached models
Files Generated
model_config.json: Quantization and optimization settingsdeployment_config.yaml: Mobile deployment configurationresource_monitor.py: Runtime resource trackingbattery_optimizer.py: Battery-aware processing logic
Integration Notes
- For offline model synchronization, use the
synchronizing-dataskill - Hardware-specific optimizations (NNAPI, CoreML) require platform-specific build configuration
- Battery monitoring patterns above are self-contained and production-ready