Architectures for Accelerated AI: A Survey of Platforms from Data Centers to Vision-Based Systems

The research evaluates modern developments in artificial intelligence (AI) and machine learning (ML) about their use in hardware acceleration platforms and data center systems and live systems including autonomous vehicle technology and recommendation engines. Efficient architectural designs for AI chips need emphasis because the industry is expected to generate $70 billion revenue by 2026. The paper shows how artificial intelligence training platforms at Facebook Zion scale up to edge computing designs such as RNNAccel. The research reviews both YOLOv3 and SSD-ResNet with CenterNet within object detection models while investigating the low-memory solution HarDNet. The paper demonstrates how machine learning has merged with vision-based autonomous systems through analyses of navigation integration. This demonstrates the developing combination between hardware systems and optimization software. Multiple research documents show that AI technologies rapidly expand through various computational settings.