Optimize Systems and Semiconductor Architecture for Deep Learning Algorithms Using System-Level Modeling
November 14 @ 10:00 am - 11:00 am PST
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).
Key Takeaways
- Trade-Off Latency, Power, and Cost Using Early Simulation
- By modeling early, teams can visualize trade-offs and make informed decisions on processor and component selection to hit project goals effectively.
- Integrate Shift-Left and Shift-Right Strategies in System-Level Modeling
- Bring software testing and design validation forward to avoid issues in later stages, enhancing the quality of final designs.
- Map Applications to Diverse Processing Units
- Learn to deploy applications seamlessly across CPUs, GPUs, TPUs, and AI engines to maximize AI’s impact while optimizing for cost and power.
- Foster Collaboration Between OEMs, Tier 1 Suppliers, and Semiconductor Manufacturers
- Use our methodology to facilitate better communication and integration across all stakeholders involved in the AI hardware design process.
Whether you’re involved in automotive, avionics, or advanced SoC architectures, this session offers an invaluable opportunity to master the nuances of system-level modeling for AI architecture and streamline your deep learning deployment.
Don’t Miss Out on Transforming Your AI Deployment Strategy!
Join us for this exclusive session and gain the insights you need to optimize your systems and semiconductor architecture for cutting-edge deep learning applications.
Date: November 14th, 2024
Session 1: 11:30 AM India / 3:00 PM Japan or Korea / 2:00 PM China
Sign up: https://bit.ly/4eZqnjP
Session 2: 10:00 AM USA PDT / 1:00 PM USA EDT
Register: https://bit.ly/3YFM82o
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- Mirabilis
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