Artificial Intelligence (AI) has transformed the world of technology, enabling systems to learn, adapt, and make decisions without explicit programming. From autonomous vehicles to medical diagnostics and flight control systems, AI promises unprecedented efficiency and capability. However, when it comes to safety-critical systems—where failure could result in injury, loss of life, or significant damage—the use of AI introduces profound challenges that go far beyond traditional software engineering. Unlike conventional software, which behaves predictably according to its programmed logic, AI is built on learning and training. Its decisions and outputs depend heavily on the data it has been trained on and the patterns it recognizes during runtime. This adaptive, data-driven behavior means that an AI system’s responses may vary with changing inputs or environments, often in ways that are not explicitly defined or foreseen by developers. While this flexibility is a strength in many applica...
In the design and verification of safety-critical systems , such as those used in avionics, automotive, defense, or medical devices, performance predictability is as essential as functional correctness. Among the many performance parameters that engineers must analyze, CPU utilization is perhaps one of the most fundamental — it defines how efficiently software uses the processor, how well timing constraints are met, and whether the system can maintain deterministic behavior under peak loads. This blog explores the most effective tools and techniques for CPU utilization profiling , emphasizing their importance and suitability for safety-critical environments , where certification, determinism, and traceability are non-negotiable.