<![CDATA[Rapid developments in artificial intelligence (AI) have driven the need for more efficient and powerful computing infrastructure, especially in cloud environments. This research explores smart strategies in providing hardware for AI solutions in the cloud, focusing on the latest innovations in AI hardware such as neuromorphic chips, FPGAs, and ASICs. Through a comprehensive analysis of the current literature, performance benchmarks, and implementation case studies, the study identifies several key strategies. Key findings include the effectiveness of hybrid architectures that combine different types of AI hardware, the potential for resource disaggregators and composable architectures to improve flexibility and efficiency, and the importance of specific acceleration for different phases in the AI pipeline. The study also emphasizes the significance of performance optimization and energy efficiency, as well as the integration of security and data privacy features in AI hardware design. Challenges such as standardization, scalability, and complexity management are discussed along with future opportunities in green AI and computing-in-memory. In conclusion, implementing a smart strategy in the provision of AI hardware in the cloud requires a holistic approach that considers workload diversity, architectural flexibility, energy efficiency, and security aspects. This research provides valuable insights for cloud service providers, hardware manufacturers, and AI practitioners in optimizing infrastructure to support AI innovation in the cloud computing era.]]>
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