AI & HPC Electronics Built for Extreme Scale
Meet the performance, power, and reliability demands of GenAI and HPC workloads with predictive hardware monitoring and deep silicon intelligence.
Meet the performance, power, and reliability demands of GenAI and HPC workloads with predictive hardware monitoring and deep silicon intelligence.
In-mission SoC intelligence, monitoring, and optimization - from hyperscale training clusters to inference deployed at the edge.
Dynamically adjust power consumption and boost frequency under functional workloads, with built-in reliability assurance.
Maximize tokens-per-watt
Increase throttle-free time
Reduce energy consumption
Control power vs. lifetime
Detect, predict, and prevent faults during system operation before they impact training runs or mission-critical inference.
Prevent functional failures
Avoid silent data corruption (SDC)
Eliminate system-wide errors
Pinpoint root cause


Ensure reliability and performance of advanced 2.5D and 3D packages used in high-density AI accelerators and edge-AI platforms.
Optimize SiP power and performance
Enable shift left decisions
Increase die-to-die interconnect reliability
Reduce SiP operational costs
Gain in-chip visibility to optimize silicon for demanding workloads from bring-up to volume production.
Reduce time to market
Optimize power and performance per-die
Increase quality and reliability
Maximize operational efficiency


Turn high-performance chips into intelligent system sensors to improve quality, performance, and deployment at scale.
Detect latent defects
Shorten time to market
Gain visibility during functional tests
Silicon to system correlation
June Paik • CEO, FuriosaAI
Eddie Ramirez • VP of Go-to-Market, Infrastructure Line of Business, Arm
Sue Ryu • CEO, SAPEON
Dr. Charlie Su • CTO and President, Andes Technology
Deep dive into our health and performance monitoring solutions for advanced AI chips and high-end HPC applications
This white paper features proteanTecs dedicated suite of embedded solutions purpose-built for AI workloads, offering applications engineered to dynamically reduce power, prevent failures and optimize throughput.
Two-stage detection approach, offering SDC prevention solutions for different stages of a chip's lifespan: ML-powered Outlier Detection for semiconductor defect detection and Real-Time Health Monitoring for in-field predictive and prescriptive maintenance.
Discover how the fabless chipmaker used proteanTecs on-tester and cloud analytics to prevent a flood of RMAs with ML-powered spatial analysis
This webinar discusses what is needed to design, manufacture and deploy advanced SoCs for AI applications today (and tomorrow).
This paper introduces proteanTecs groundbreaking Outlier Detection solution that eliminates that tradeoff. proteanTecs' Outlier Detection uses deep data analytics and ML to detect latent defects as early as Wafer Sort, achieving high fault detection accuracy by learning normal behavior with on-chip agents and comparing test measurements with predicted ones. It identifies marginal issues beyond simple pass/fail metrics, where traditional methods fail.