Athena I/O Expansion | Athens Mfg.

Perception Modules

Vision Module

Camera module for visual perception. Object detection, scene understanding, motion tracking, facial recognition, OCR. Sensor selection based on deployment requirements.

Audio Module

Microphone array for audio perception. Beamforming, voice activity detection, speech-to-text, speaker identification, sound event detection. Configuration scaled to environment.

Combined Perception Hub

All-in-one: camera + microphone + compute. Complete perception pipeline, local AI processing (no cloud), privacy-focused design, distributed deployment.

Architecture

Data Flow

Video Pipeline

Camera → USB/CSI → CPU → Bridge → NPU (inference) → CPU ← Bridge ← NPU → Local storage / API → Knowledge Graph

Audio Pipeline

Mic Array → USB/I2S → CPU → Bridge → NPU (inference) → CPU ← Bridge ← NPU → Transcription / API → Knowledge Graph

Cross-Modal Fusion

Video + Audio → CPU (temporal alignment) → Bridge → NPU (fusion) → Unified scene understanding → Knowledge Graph

Software Stack

CPU/GPU Side

class AthenaIOService: """Manages I/O devices and feeds data to NPU.""" def on_frame(self, frame, timestamp): # Send to NPU for inference result = self.bridge.send_query({ 'type': 'video_inference', 'frame': frame, 'timestamp': timestamp }) # Store in knowledge graph self.store_observation('video', frame, result, timestamp) def on_audio(self, audio, timestamp): result = self.bridge.send_query({ 'type': 'audio_inference', 'audio': audio, 'timestamp': timestamp }) self.store_observation('audio', audio, result, timestamp)

NPU Side

class VideoInference: """Process video frames on NPU.""" def process_frame(self, frame): objects = self.yolo.detect(frame) # Object detection scene_embedding = self.clip.encode(frame) # Scene understanding scene_desc = self.clip.describe(frame) # Scene description return {'objects': objects, 'embedding': scene_embedding, 'description': scene_desc} class AudioInference: """Process audio on NPU.""" def process_audio(self, audio, sample_rate=16000): has_voice = self.vad.detect(audio, sample_rate) if has_voice: transcript = self.whisper.transcribe(audio, sample_rate) embedding = self.encoder.encode(transcript) return {'transcript': transcript, 'embedding': embedding} event = self.classify_audio_event(audio, sample_rate) return {'audio_event': event}

Deployment Configurations

Single Perception Unit

Vision or audio module connected to Athena compute. Individual workspace monitoring, entry-level perception.

Multi-Sensor Array

Multiple vision and audio modules across a facility. Full spatial awareness, cross-modal fusion.

Industrial Perception

Ruggedized sensors for manufacturing QC, pharmaceutical inspection, aerospace monitoring. Configured for specific industry requirements.

Custom Configuration

Sensor selection and placement based on deployment domain. Every environment is different — perception scales to the job.

Development Roadmap

Phase 1: Vision MVP (Q4 2026)

Camera module, video capture pipeline, object detection inference, basic scene understanding, local storage + API.

Phase 2: Audio MVP (Q1 2027)

Microphone array, audio capture pipeline, speech-to-text inference, basic transcription, voice activity detection.

Phase 3: Cross-Modal Fusion (Q2 2027)

Temporal alignment, cross-modal inference, scene understanding, knowledge graph integration, developer SDK.

Phase 4: Production (Q3 2027)

Industrial design, manufacturing setup, quality testing, partner integrations, deployment support.

Open Source Strategy

Hardware: CERN-OHL-P (open hardware). Software: MIT License. Documentation: CC-BY-SA 4.0.