In Memory Computing Hardware Accelerators For Data Intensive Applications
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In-Memory Computing Hardware Accelerators for Data-Intensive Applications
This book describes the state-of-the-art of technology and research on In-Memory Computing Hardware Accelerators for Data-Intensive Applications. The authors discuss how processing-centric computing has become insufficient to meet target requirements and how Memory-centric computing may be better suited for the needs of current applications. This reveals for readers how current and emerging memory technologies are causing a shift in the computing paradigm. The authors do deep-dive discussions on volatile and non-volatile memory technologies, covering their basic memory cell structures, operations, different computational memory designs and the challenges associated with them. Specific case studies and potential applications are provided along with their current status and commercial availability in the market.
Recent Advances in Neuromorphic Computing
Artificial Intelligence (AI) is a transformative technology that reshapes our daily lives. Machine Learning (ML), the engine of such a revolution, empowers computers to learn from data, driving innovation in areas such as medicine, robotics, and smart cities through edge applications. These applications bring AI processing closer to the data source, enabling real-time insights and decisions. This evolution is fueled by advancements in hardware and architecture: (1) neuromorphic computing promises unparalleled efficiency; (2) in-memory computing eliminates data access bottlenecks, while emerging memory materials offer denser, faster, and more energy-efficient storage. Looking ahead, AI promises even more profound changes. For instance, explainable AI will make decision-making more transparent, and truly autonomous systems will adapt to unforeseen circumstances. Last but not least, the convergence of AI with quantum computing could unlock entirely new possibilities. This journey showcases a deep understanding of both the theoretical foundations and practical applications of AI. It also demands careful consideration of ethical implications and a commitment to responsible development, ensuring that AI benefits all of humanity.
Data-Intensive Computing
Describes principles of the emerging field of data-intensive computing, along with methods for designing, managing and analyzing the big data sets of today.