Technology Deep Dive

Built Different. Built Better.

The LVS-250 represents a fundamental rethinking of edge AI architecture, combining cutting-edge semiconductor technology with purpose-built security.

Chiplet Architecture
Post-Quantum Security
Integrated RF
Powerful SDK
Architecture

UCIe 2.0 Chiplet Architecture

A revolutionary approach to edge AI processing that combines the benefits of modular design with breakthrough performance.

Neural Compute
230 TOPS
Security
Post-Quantum
RF Modem
SDR
Beamformer
Phased Array
MRAM
256 MB
UCIe 2.0 Interconnect Fabric32 GT/s per lane

UCIe 2.0 Interconnect

Industry-standard chiplet interconnect providing 32 GT/s bandwidth per lane with latency under 2ns.

  • Modular design flexibility
  • Independent chiplet optimization
  • Future-proof scalability
  • Reduced development time

GlobalFoundries 12LP+

Manufactured on GF's proven 12LP+ process, optimized for defense applications with superior reliability and availability.

  • Domestic US manufacturing
  • Defense-grade reliability
  • Optimized power efficiency
  • Secure supply chain

Heterogeneous Integration

Each chiplet is optimized for its specific function, enabling best-in-class performance across all capabilities.

  • Specialized compute units
  • Dedicated security processor
  • Integrated RF subsystem
  • Non-volatile MRAM
Fleet Intelligence

Collective Intelligence

Each LVS-250 node operates autonomously while contributing to shared fleet awareness, enabling swarm coordination, distributed sensor fusion, and collaborative decision-making.

Fleet Intelligence
UAV
UGV
USV
Fixed installations

Mesh Networking

Ad-hoc mesh formation for swarm operations with automatic node discovery and routing.

Distributed Sensor Fusion

Multi-platform sensor fusion combining vision, RF, and positioning data across the fleet.

Federated Learning

Models improve collectively without centralizing sensitive data. Each node learns and shares insights.

PQC Fleet Messaging

Post-quantum encrypted communications ensure secure coordination even against future threats.

Federated Learning

Traditional ML requires centralizing all training data, which creates security and bandwidth problems for defense applications. Federated learning keeps data local while sharing model improvements.

  • Data never leaves the edge device
  • Models improve from collective experience
  • Bandwidth-efficient gradient sharing
  • PQC-encrypted model updates
100x
Less Bandwidth vs. Centralized
Zero
Raw Data Transmitted
Security

Post-Quantum Security

Built from the ground up with security as a first-class requirement, not an afterthought. Ready for the post-quantum era.

Post-Quantum Cryptography

CRYSTALS-Kyber and CRYSTALS-Dilithium algorithms protect against future quantum computer attacks.

Hardware Root of Trust

Immutable boot chain starting from silicon ensures only authenticated code executes.

Remote Attestation

Cryptographically prove system integrity to command and control infrastructure.

Tamper Detection

Physical and logical tamper detection with automatic key zeroization.

Secure Enclave

Isolated execution environment for sensitive operations and key management.

FIPS 140-3 Ready

Designed to meet FIPS 140-3 Level 3 requirements for government applications.

Quantum-Resistant Today

While quantum computers capable of breaking current encryption are still years away, adversaries are already harvesting encrypted data for future decryption. The LVS-250 protects your data against both current and future threats.

Developer Tools

Powerful SDK

Deploy your models in minutes, not months. Our SDK provides everything you need to build, test, and deploy edge AI applications.

detection_demo.py
 1  class= "text-pink-400">class= "text-purple">from lvs   class= "text-pink-400">class= "text-purple">import LVS250, Model
 2
 3 # Initialize device
 4device = LVS250.connect()
 5
 6 # Load pre-tra  class= "text-pink-400">class= "text-purple">ined model
 7model = Model.load( "yolov8-nano- class="text-pink-400 "defense")
 8
 9 # Configure detection pipel  class= "text-pink-400">class= "text-purple">ine
10pipel  class= "text-pink-400">class= "text-purple">ine = device.create_pipel  class= "text-pink-400">class= "text-purple">ine(
11    model=model,
12      class= "text-pink-400">class= "text-purple">input_source= "mipi-csi",
13    output_  class= "text-pink-400">class= "text-purple">format= "detections"
14)
15
16 # Run   class= "text-pink-400">class= "text-purple">inference
17  class= "text-pink-400">class= "text-purple">for frame   class= "text-pink-400">class= "text-purple">in pipel  class= "text-pink-400">class= "text-purple">ine.run():
18      class= "text-pink-400">class= "text-purple">for detection   class= "text-pink-400">class= "text-purple">in frame.detections:
19        pr  class= "text-pink-400">class= "text-purple">int(f "{detection. class="text-pink-400 "class_name}: {detection.confidence:.2f}")
20         # Send to C2 system
21          class= "text-pink-400">class= "text-purple">if detection.confidence > 0.85:
22            send_alert(detection)

CLI Tools

Powerful command-line tools for model deployment and device management.

Python SDK

Familiar APIs for ML engineers to deploy PyTorch and TensorFlow models.

Model Zoo

Pre-optimized models for common detection and classification tasks.

Documentation

Comprehensive guides, tutorials, and API references.

Ready to Transform Your Edge AI Capabilities?

Schedule a demo to see the LVS-250 in action. Our team will show you how next-generation edge AI can accelerate your mission.

Questions? Email us at info@lolavisionsystems.com