ENI / EAI
Neural Interface
& Edge AI
Run AI models directly on microcontrollers. Connect brain-computer interface devices. Deploy an AI agent on every EoS device — all without cloud dependency.
EAI Engine
On-device AI inference — no cloud required
EAI is an in-development embedded inference runtime. Memory requirements depend on model size, architecture, runtime overhead, and target hardware. Research areas include vision, NLP, time-series classification, and anomaly detection.
Runtimes
TFLite · ONNX · GGUF
Quantization
4-bit, 8-bit, FP16
Min RAM
256KB (classification)
LLM Memory
Model- and hardware-dependent
Tasks
Vision · NLP · Time-series · Anomaly
Acceleration
CMSIS-NN · Helium · RISC-V V-ext
eai-demo.py
>>> eai.load('gesture_classifier.tflite')
>>> eai.run(sensor_data)
{'class': 'wave_right', 'confidence': 0.97}
>>> eai.load('phi3-mini-4bit.gguf')
>>> eai.chat('What is the sensor reading?')
'Temperature is 23.4°C, humidity 61%. All nominal.'
Use Cases
What you can build with ENI/EAI
Medical Wearables
Classify ECG arrhythmias, detect falls, and monitor sleep stages — all on-device with no PHI leaving the hardware.
Brain-Computer Interface
Read motor imagery signals from EEG headsets and translate them into device commands via ENI.
Industrial Predictive Maintenance
Run vibration anomaly detection models on factory sensors. Alert before failures occur.
Edge Vision
Object detection and image classification on camera modules with no cloud upload.
AI-Assisted Debugging
eBot reads device logs and suggests fixes in plain language. Ship firmware faster.
Secure AI Inference
Models run in isolated memory partitions. Inference results are signed and attestable.
Run AI on your embedded device today
EAI and ENI are open-source research components under active development for EoS integrations.