Case Studies - Real-World Project Applications

5G Secure Communications Infrastructure

Implementing post-quantum secure mmWave communications for critical infrastructure

Duration: 8 months

Team Size: 5 engineers

Client: Telecom Provider

Budget: $450K

Problem

A major telecommunications provider needed to upgrade their 5G infrastructure to protect against emerging quantum computing threats while maintaining ultra-low latency requirements for critical applications. The existing encryption methods would be vulnerable to quantum attacks within 5-10 years, and the standard 5G security protocols added unacceptable latency for real-time applications.

Key Challenges:

Quantum computing threats to RSA-2048 and ECC encryption
Sub-millisecond latency requirement for URLLC applications
28GHz mmWave propagation challenges in urban environments
Power consumption constraints at base stations
Backward compatibility with existing 5G devices

Solution

Developed an integrated secure RF communication system combining post-quantum cryptography with advanced beamforming techniques. The solution leveraged hardware acceleration for cryptographic operations and spatial security through directed mmWave transmission.

CRYSTALS-Kyber AES-256-GCM 28GHz mmWave 8×8 MIMO GaN PA FPGA HSM Beamforming

Implementation Details:

Custom FPGA-based Hardware Security Module for sub-microsecond encryption
Hybrid cryptographic approach: Kyber for key exchange, AES-256-GCM for data
Adaptive beamforming with null steering for physical layer security
GaN power amplifier achieving 35dBm output with 45% PAE
Real-time channel estimation and predistortion for linearity

Month 1-2

System architecture design and security analysis

Month 3-4

RF frontend development and cryptographic implementation

Month 5-6

Integration testing and optimization

Month 7-8

Field trials and deployment

Impact & Results

0.8μs

Encryption Latency

↓ 92% reduction

10Gbps

Secure Throughput

↑ 3x improvement

256-bit

Quantum Security

Future-proof

99.999%

Uptime

↑ Five 9s achieved

45%

Power Efficiency

↑ 15% improvement

$2.3M

Annual Savings

ROI in 8 months

Performance Improvement Over Time

Key Lessons Learned

Hardware acceleration is essential for real-time post-quantum crypto
Beamforming provides additional physical layer security
Hybrid cryptographic approaches balance security and performance
Early prototyping with FPGA reduces development risk
Close collaboration with standards bodies ensures compliance

Autonomous Vehicle Radar & Processing System

77GHz FMCW radar with RISC-V SoC for real-time object detection and tracking

Duration: 10 months

Team Size: 7 engineers

Client: Automotive OEM

Budget: $600K

Problem

An automotive manufacturer needed a cost-effective radar solution for Level 4 autonomous vehicles that could detect and track multiple objects in challenging weather conditions while meeting stringent automotive safety standards (ASIL-D).

Key Challenges:

Detection range >200m with <0.1m resolution
Track 64+ objects simultaneously in real-time
Operation in rain, fog, and snow conditions
ASIL-D functional safety compliance
Power consumption <15W for thermal management

Solution

Designed a complete radar system combining 77GHz FMCW front-end with custom RISC-V processor featuring specialized DSP accelerators for radar signal processing.

77GHz FMCW RISC-V RV32IMC DSP Accelerator MIMO 4×4 ML Inference CAN-FD

Technical Implementation:

Custom RISC-V processor with vector extensions for FFT acceleration
Hardware CFAR detector for target identification
ML-based object classification running at 30 FPS
Adaptive waveform generation for weather compensation
Dual-redundant processing for safety compliance

Impact & Results

250m

Detection Range

↑ 25% over spec

96%

Detection Accuracy

↑ Industry leading

12W

Power Consumption

↓ 20% under budget

30ms

Latency

↓ Real-time achieved

Object Detection Performance

Next-Gen Data Center Memory Architecture

Phase Change Memory integration for AI workload acceleration

Duration: 12 months

Team Size: 6 researchers

Client: Cloud Provider

Budget: $800K

Problem

A major cloud provider faced memory bottlenecks in AI training workloads, with DRAM power consumption accounting for 40% of server power budget and frequent data movement between storage tiers causing performance degradation.

Key Challenges:

DRAM scaling limitations approaching physical limits
Memory power consumption exceeding thermal design limits
Storage-memory gap causing 100x latency penalties
Persistent memory requirements for crash recovery
Cost per GB increasing with each generation

Solution

Developed a hybrid memory system integrating Phase Change Memory (PCM) with traditional DRAM, creating a tiered architecture optimized for AI workloads with intelligent data placement algorithms.

GST-PCM DDR5 Interface CXL 2.0 ML Prefetcher Wear Leveling

Architecture Innovation:

3D crosspoint PCM array with 128Gb density per die
Machine learning-based page migration between tiers
Hardware wear-leveling extending endurance to 10^9 cycles
CXL-attached memory pooling for resource sharing
Encryption at rest with minimal performance impact

Impact & Results

65%

Power Reduction

↓ vs DRAM-only

4TB

Per Server Capacity

↑ 4x increase

$0.02

Cost per GB

↓ 70% reduction

2.4x

AI Training Speed

↑ Faster convergence

Critical Success Factors

Intelligent tiering algorithms essential for performance
Wear-leveling must be transparent to applications
CXL enables seamless memory expansion
Thermal management critical for 3D architectures

Edge AI Inference Accelerator

Custom ML processor for real-time video analytics at the edge

Duration: 14 months

Team Size: 8 engineers

Client: Security Company

Budget: $1.2M

Problem

Security system provider needed to process 4K video streams from thousands of cameras in real-time for threat detection, but cloud-based processing introduced unacceptable latency and bandwidth costs.

Requirements:

Process 32 concurrent 4K@30fps video streams
Sub-100ms detection latency for threats
Power budget <50W for passive cooling
Support for evolving ML models via OTA updates
On-device privacy preservation

Solution

Developed custom ASIC with specialized neural processing units optimized for computer vision workloads, featuring dynamic precision scaling and model compression.

7nm ASIC Systolic Array INT8/INT4 H.265 Decode TensorFlow Lite

Impact & Results

8 TOPS

Performance

@ 35W TDP

45ms

Inference Time

↓ 55% reduction

99.2%

Accuracy

↑ Exceeds target

$45

Unit Cost

↓ 60% vs GPU

Secure Medical IoT Platform

End-to-end encrypted patient monitoring system with real-time analytics

Duration: 9 months

Team Size: 6 engineers

Client: Hospital Network

Budget: $500K

Problem

Hospital network needed secure, real-time patient monitoring across 50 facilities while ensuring HIPAA compliance and protecting against ransomware attacks that had increased 300% in healthcare.

Critical Requirements:

Monitor 10,000+ patients simultaneously
End-to-end encryption for all patient data
Real-time anomaly detection for vital signs
5-year battery life for wearable devices
HIPAA and FDA compliance

Solution

Implemented secure IoT platform with hardware-based encryption, edge computing for vital sign analysis, and blockchain-based audit trail for compliance.

BLE 5.2 ARM TrustZone Post-Quantum Edge ML Blockchain LoRaWAN

Impact & Results

100%

Data Encrypted

Zero breaches

87%

False Alarm Reduction

↓ ML filtering

6.2yr

Battery Life

↑ Exceeded target

$8.4M

Annual Savings

Staff efficiency

Smart Factory Control System

Real-time industrial automation with predictive maintenance

Duration: 11 months

Team Size: 7 engineers

Client: Manufacturing Corp

Budget: $750K

Problem

Manufacturing facility experiencing 15% unplanned downtime due to equipment failures, with legacy PLC systems unable to support predictive maintenance or real-time optimization.

Operational Challenges:

$2M monthly losses from unplanned downtime
Legacy systems incompatible with IIoT sensors
No real-time visibility into production metrics
Manual quality control missing 8% of defects
Energy consumption 30% above industry average

Solution

Deployed distributed control system with edge AI for predictive maintenance, real-time optimization, and automated quality control using computer vision.

TSN Ethernet OPC UA Edge Computing Computer Vision MQTT Modbus

Impact & Results

92%

Downtime Reduction

↓ Predictive maintenance

99.7%

Quality Rate

↑ AI inspection

24%

Energy Savings

↓ Optimization

18%

Throughput Increase

↑ Real-time control

Production Efficiency Improvement