Quantize, prune, and distill models for ARM devices
Runtime InspectorActive
Multi-agent profiling and diagnostics
Memory EngineIdle
On-device RAG for semantic search
Battery SchedulerActive
ML-based power management
IoT ConnectorIdle
BLE, Thread, Matter device integration
Privacy FirewallActive
Sandboxed execution and policy enforcement
System Log
[09:41:00]ARM Engine initialized
[09:41:01]Runtime Inspector started
[09:41:02]Privacy Firewall enabled
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Model Compressor
Original
Compressed
Compression: 4.0x
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Runtime Inspector
Profile
Thermal
Graph
CPU Usage:45.2%
GPU Usage:62.8%
NPU Usage:38.5%
Memory:245 MB
Bottleneck:GPU
Status:Normal
Max Temp:42.3°C
Diagnosis:
Your model is bottlenecked by depthwise convolution operations. The attention mechanism consumes 35% of total execution time. Consider using fused kernels and operator fusion to reduce memory transfers.
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Memory Engine
Index
Query
Stats
Document indexed successfully! ID: Chunks:
Result:
Total Documents:0
Total Chunks:0
Storage Used:0 MB
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Battery Scheduler
Estimated Drain:2.3%
Execution Time:~5 minutes
Recommended:Execute Now
Tip:
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IoT Connector
Available Devices:
Sensor Data:
Accelerometer: X: 0.0, Y: 0.0, Z: 9.8
Heart Rate:72 bpm
Activity:Standing
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Privacy Firewall
All AI operations are executed locally. Zero data leaves your device.
Local Processing Only✓ Enabled
Network Access✗ Blocked
Data Encryption✓ Active
Audit Logging✓ Enabled
Audit Complete:
No privacy violations detected.
All operations comply with policies.