In a world where artificial intelligence and machine learning are embedded in critical applications—from real-time tracking and object detection to autonomous systems—the architecture behind these innovations must be both powerful and efficient. To help engineers and architects address these challenges, our upcoming webinar will demonstrate how System-Level Modeling can be a game-changer in optimizing the performance and power efficiency of deep learning algorithms, including Deep and Convolutional Neural Networks (DNNs and CNNs).

Through system-level modeling, design teams can analyze and optimize critical factors such as response time, power consumption, component selection, and cost-effectiveness before finalizing their designs. This session is particularly beneficial for SoC architects, embedded systems designers, and other professionals working to balance performance, power, and cost for AI deployments in demanding environments.

What You’ll Learn

With AI systems like CNNs now integral to technologies in real-time tracking, object detection, and autonomous navigation, the need for architecture trade-offs has intensified. Our approach to system-level modeling allows teams to:

• Evaluate Hardware Combinations: Assess combinations of CPUs, GPUs, AI-specific processing units, and standalone FPGAs to select the best configuration for your needs.
• Optimize Task Partitioning: Partition tasks across chips to achieve targeted performance without compromising power efficiency or exceeding budget constraints.
• Realistic Workload Simulation: Use cycle-accurate models to depict AI/ML algorithm performance under real-world conditions, creating accurate simulations of hardware components in action.

Through detailed case studies across industries like automotive, avionics, data centers, and radar systems, you’ll see how this methodology applies to diverse scenarios, helping to trade off key performance indicators (e.g., vehicle mileage vs. processing power).