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World Congress 2023 • Aug 11, 2023

Trends, Challenges and Best Practices for AI at the Edge

Ekaterina Sirazitdinova

How do you deploy zero-latency AI on devices with strict compute and power limits? Learn to master quantization, pruning, and NVIDIA frameworks to conquer edge deployment.

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#1 about 7 min

Defining edge AI and its widespread industry applications

Localized data processing enables independent automation and real-time computation across manufacturing, retail, and healthcare.

#2 about 4 min

Hardware constraints and core challenges in edge environments

Operating intelligent models on embedded hardware requires balancing limited compute and power capacities with strict low-latency requirements.

#3 about 2 min

Increasing model accuracy through synthetic data generation

Augmenting realistic scenarios with synthetic data solves expensive manual labeling and effectively covers anomalous edge cases.

#4 about 7 min

Optimizing edge model throughput to ensure real-time performance

Techniques like precision quantization, network pruning, and graph optimization reduce model footprint and accelerate execution on constrained hardware.

#5 about 5 min

Accelerating AI development with software and pretrained models

Utilizing managed toolkits and foundational templates simplifies custom model training, synthetic data generation, and complex multi-node orchestration.

#6 about 6 min

Deploying optimized models for high-throughput video analytics

Specialized deployment SDKs handle layer fusion, memory management, and concurrent sequence execution for complex multi-model pipelines.

#7 about 2 min

Exploring domain gaps and hardware requirements for deployment

Correcting for texturing differences between synthetic and real datasets ensures higher accuracy across various processing units.

Matching moments

2:18 min

The necessity of Edge AI and its driving constraints

Sasha Denisov Sasha Denisov +1 · World Congress 2026 Europe

1:56 min

Optimizing AI processing capabilities for edge microcontrollers

Stephan Gillich Stephan Gillich +3 · World Congress 2024

3:21 min

Reducing cloud dependency with on-device edge AI models

Precious Osaro Precious Osaro · World Congress 2026 Europe

2:26 min

Cost and latency pressures pushing AI to the edge

Moe Sani Moe Sani · World Congress 2026 Europe

1:09 min

Deploying algorithms and AI models to edge production

Irina Terekhova Irina Terekhova · World Congress 2026 Europe

3:19 min

Technological shifts enabling practical edge AI deployment

Sasha Denisov Sasha Denisov · World Congress 2026 Europe

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