The Neural Network
IS the Circuit
FPGA-native AI inference. Deterministic. Microsecond latency. Microwatt power. We crystallize neural network weights directly into FPGA fabric topology.
97.89%
Accuracy
2,302
LUTs
<1µs
Latency
µW
Power
Deterministic
No OS. No scheduler. No cache misses. Guaranteed latency every single inference cycle.
Efficient
Microwatt-level power consumption. Run inference at the edge where GPUs cannot survive.
Resilient
Radiation-tolerant by architecture. No stored weights to corrupt. Mission-ready by design.
Core Technology
Bio-Inspired Neural Networks
crystallized into silicon
Not GPU inference ported to FPGA — the neural network topology IS the circuit topology.
BIHN Architecture
Bio-Inspired Hardware Neural Networks
BIHN crystallizes trained neural network weights directly into FPGA lookup table topology. The neural network doesn't run on the hardware — it becomes the hardware. Wire-speed inference as fast as data arrives, with zero external memory dependency.
- Weights encoded directly as LUT configurations
- No weight memory fetches during inference
- Combinational logic propagation at wire speed
- Inherently parallel computation paths
- Zero external memory dependency
NALA Framework
Neuromorphic Adaptive Learning Architecture
NALA provides the runtime framework for deploying, managing, and adapting BIHN networks in the field. It enables runtime adaptation without downtime and maintains deterministic latency guarantees throughout the system lifecycle.
- Deterministic inference timing guarantees
- Extreme power efficiency at microwatt scale
- Runtime reconfiguration without downtime
- Radiation-tolerant by architectural design
- Field-adaptable without full redeployment
Architecture Comparison
Traditional GPU
- OS + scheduler overhead
- Memory bus bottleneck
- Kilowatt power draw
- Cache miss variability
- Non-deterministic latency
FPGA Accelerator
- Still runs software models
- Still fetches weights from memory
- Better but not native
- Reduced but not zero jitter
- Intermediate power savings
Nyx BIHN
- No OS — pure hardware logic
- Weights ARE the circuit
- Microwatt power draw
- Wire-speed, zero-jitter inference
- Radiation-tolerant by design
Performance
Proven on Silicon
Real results from the BIHN architecture running on AMD Kria KD240 evaluation hardware. No simulations. No projections. Measured performance.
97.89%
MNIST Accuracy
Classification accuracy achieved with BIHN architecture on standard benchmark
2,302
LUTs Used
Out of ~100K+ available on AMD Kria KD240 — massive scaling headroom
<1µs
Inference Latency
Deterministic — no OS, no scheduler, no cache misses, no jitter
µW
Power Draw
Microwatt-level consumption vs GPU kilowatts — zero external memory
Benchmarked on AMD Kria KD240 — utilizing only 2,302 of ~100K+ available LUTs. Massive headroom for scaling network complexity.
Target Markets
Where Determinism Matters
Mission-critical environments where non-deterministic inference is not an option.
Defense
Mission-critical AI at the tactical edge. Radiation-tolerant, deterministic, and operating at wire speed for contested environments.
- Naval edge AI
- Autonomous systems
- Radar processing
- Electronic warfare
- Missile guidance
Automotive
Real-time perception and decision-making at microwatt power budgets for next-generation autonomous and connected vehicles.
- Real-time sensor fusion
- ADAS
- Fleet intelligence at the edge
- V2X communications
Industrial
Always-on inference for process optimization, predictive maintenance, and quality control in harsh operating environments.
- Predictive maintenance
- Quality inspection
- Process control
- Anomaly detection
Robotics
Ultra-low-latency perception and decision-making for autonomous systems operating in the physical world.
- Thermal safety systems
- Real-time perception
- Autonomous navigation
- Swarm coordination
Products & Platforms
From Design to Deployment
A complete toolchain for FPGA-native AI inference — from development to production hardware.
NyxForge
AI-Assisted FPGA Development PlatformAccelerate FPGA-native AI development with automated architecture exploration, weight crystallization, and deployment tooling. From neural network design to silicon in hours, not months. NyxForge automates the translation of trained models into optimized BIHN hardware configurations.
The Veil
Secure Communications PlatformHardware-accelerated secure communications with AES-GCM encryption and Solana blockchain trust anchoring. Designed for environments where compromise is not an option. The Veil provides end-to-end encrypted messaging with cryptographic proof of message integrity and delivery.
BIHN Development Kit
AMD Kria KD240 Evaluation HardwareGet hands-on with FPGA-native AI inference. The BIHN Dev Kit includes an AMD Kria KD240 evaluation board, pre-built BIHN reference designs, comprehensive documentation, and example applications for rapid prototyping and proof-of-concept development.
About
Built by Engineers,
for Engineers
John Schmotzer
Founder & CEO
20 years of systems engineering across the defense, automotive, and semiconductor industries. Deep expertise in FPGA architecture, real-time systems, and AI/ML at the edge.
From automated vehicle systems and Patriot missile defense at Raytheon, to fleet data architecture at Ford, to GPU architecture at NVIDIA — John brings deep cross-domain expertise to the challenge of edge AI inference.
The Company
- HeadquartersAustin, Texas
- StructureC-Corporation
- FocusFPGA-native AI inference
- PlatformAMD Kria / Xilinx Adaptive SoC
- SectorsDefense, Automotive, Industrial, Robotics
“GPUs are a terrible fit for edge AI inference. The future belongs to purpose-built silicon that encodes intelligence directly into hardware topology.”
Contact
Ready to Deploy
AI at the Edge?
Whether you're evaluating FPGA-native inference for defense applications, autonomous systems, or industrial edge AI — we'd like to hear from you.
Defense & Enterprise
For classified program inquiries, partnership opportunities, or custom FPGA inference requirements, please reach out directly.