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The explosion of “big data” applications imposes severe challenges of speed and scalability on traditional computer systems. As the performance of traditional Von Neumann machines is greatly hindered by the increasing performance gap between CPU and memory (“known as the memory wall”), neuromorphic computing systems have gained considerable attention. The biology-plausible computing paradigm carries out computing by emulating the charging/discharging process of neuron and synapse potential. The unique spike domain information encoding enables asynchronous event driven computation and communication, and hence has the potential for very high energy efficiency. This survey reviews computing models and hardware platforms of existing neuromorphic computing systems. Neuron and synapse models are first introduced, followed by the discussion on how they will affect hardware design. Case studies of several representative hardware platforms, including their architecture and software ecosystems, are further presented. Lastly we present several future research directions.
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This content will become publicly available on November 21, 2025
Data Management: Interactions with Computer Architecture and Systems
This guide illuminates the intricate relationship between data management, computer architecture, and system software. It traces the evolution of computing to today's data-centric focus and underscores the importance of hardware-software co-design in achieving efficient data processing systems with high throughput and low latency. The thorough coverage includes topics such as logical data formats, memory architecture, GPU programming, and the innovative use of ray tracing in computational tasks. Special emphasis is placed on minimizing data movement within memory hierarchies and optimizing data storage and retrieval. Tailored for professionals and students in computer science, this book combines theoretical foundations with practical applications, making it an indispensable resource for anyone wanting to master the synergies between data management and computing infrastructure.
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- Award ID(s):
- 2310510
- PAR ID:
- 10600547
- Publisher / Repository:
- Cambridge University Press
- Date Published:
- ISBN:
- 9781009123310
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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