Athena Hardware
Purpose-built compute for distributed manufacturing intelligence
Design Philosophy
Modular, scalable compute units containing CPU/GPU and accelerator domains. Multiple units connect via high-bandwidth interconnects for distributed computing. Each unit is self-contained; stacking adds capacity. Form factor adapts to deployment requirements — not a fixed box.
Intel and Nvidia are preferred partners for CPU/GPU and simulation environments, but all component choices remain open to competitive evaluation. The architecture is vendor-agnostic by design.
Dual-Domain Architecture
CPU/GPU Domain
Traditional computing. Operating system, I/O management, user interface, application processing, simulation environments. Isolated from accelerator domain.
Accelerator Domain (Isolated)
Neural processing. AI inference, knowledge graph operations, GNN reasoning. Direct access to Graph Storage. Hardware-enforced isolation from CPU domain.
Bridge Controller
Hardware-enforced isolation between domains. Message queue, DMA engine, protocol translation. High bandwidth, low latency. Specific implementation TBD based on partner selection.
Compute Platform
Preferred: Intel + Nvidia
Intel CPUs with integrated graphics provide the simulation environments Athena needs for training and testing. Nvidia GPUs/NPUs provide the inference performance for real-world deployment.
CPU Platform
- Intel Core Ultra — Integrated NPU for edge inference
- Intel Xeon — Workstation/server deployments
- Open to alternatives based on deployment needs
GPU/Accelerator
- Nvidia Jetson — Edge AI with CUDA support
- Nvidia RTX — Workstation simulation + inference
- Nvidia Data Center — Cloud/enterprise deployments
- Open to competitive accelerators (AMD, Intel, custom)
Why Intel + Nvidia
- CUDA ecosystem: Mature tooling for AI training and simulation
- Isaac Sim integration: Nvidia's robotics simulation platform
- Omniverse: Digital twin and simulation environments
- Intel oneAPI: Cross-architecture development
- OpenVINO: Optimized inference on Intel hardware
NPU/Accelerator Options
The accelerator domain remains open to competitive evaluation. Options include:
- Nvidia NPU/TensorRT — Preferred for simulation integration
- Intel Movidius/VPU — Edge inference
- AMD Xilinx — FPGA-based acceleration
- Hailo, Groq, Cerebras — Purpose-built AI accelerators
- Custom silicon — Future possibility
Selection will be based on performance, power efficiency, tooling maturity, and partnership opportunities.
Core Components
System Storage
NVMe SSD for operating system, applications, and local data. Capacity scaled to deployment requirements.
Graph Storage
High-speed NVMe for knowledge graph. Direct access by accelerator domain. Capacity based on graph size requirements.
Memory
DDR5 system RAM. Dedicated accelerator memory (HBM or LPDDR). Capacity scaled to workload.
Networking
Mesh radio for swarm communication. Ethernet for wired backbone. Optional 5G for remote connectivity.
Deployment Configurations
Hardware scales to deployment requirements. Not fixed SKUs — each deployment is configured for its specific needs.
Edge Unit
Compact form factor for individual workspaces, IoT gateways, or small manufacturing cells. Low power, minimal footprint.
Workstation
Mid-range compute for R&D, simulation, and development. Intel Xeon + Nvidia RTX for training environments.
Rack Mount
High-density compute for enterprise deployments, data centers, or large-scale manufacturing facilities.
Scaling
Single Unit
Personal Athena. CPU/GPU + accelerator + storage. Individual AI assistant with local knowledge graph.
Multi-Unit
Enhanced Athena. Multiple units connected via high-bandwidth interconnect. Increased throughput and graph capacity.
Swarm
Distributed Athena. Multiple hosts coordinating via mesh network. Enterprise-scale deployment across facilities.