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Data flow computing --- Computer organization. --- Data flow computing.
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Computer architecture --- Data flow computing --- Computer architecture. --- Data flow computing.
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This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.
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Stream processing is a novel distributed computing paradigm that supports the gathering, processing and analysis of high-volume, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field. This book introduces the key components of the stream computing paradigm, including the distributed system infrastructure, the programming model, design patterns and streaming analytics. The explanation of the underlying theoretical principles, illustrative examples and implementations using the IBM InfoSphere Streams SPL language and real-world case studies provide students and practitioners with a comprehensive understanding of such applications and the middleware that supports them.
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Since its first volume in 1960, Advances in Computers has presented detailed coverage of innovations in computer hardware, software, theory, design, and applications. It has also provided contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles usually allow. As a result, many articles have become standard references that continue to be of significant, lasting value in this rapidly expanding field. In-depth surveys and tutorials on new computer technology. Well-known authors and researchers in the field. Extensive bibliographies with m
Data flow computing. --- Computer architecture. --- Architecture, Computer --- Dataflow computing --- Electronic data processing
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Computer architecture --- Data flow computing --- Functional programming (Computer science) --- Congresses. --- Congresses. --- Congresses.
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Data flow computing. --- Embedded computer systems --- Image processing. --- Design and construction.
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Computer architecture --- Electronic digital computers --- Parallel processing (Electronic computers) --- Ordinateurs --- Programmation des ordinateurs --- Parallélisme (Informatique) --- Programming --- Architecture --- Data flow computing --- Dataflow computing --- Electronic data processing --- Architecture, Computer --- 681.3*C13 --- data --- programmeren --- Adaptable architectures; capability architectures; cellular architecture; data flow architectures; high-level language architectures; stack-oriented processors --- Computer architecture. --- Data flow computing. --- 681.3*C13 Adaptable architectures; capability architectures; cellular architecture; data flow architectures; high-level language architectures; stack-oriented processors --- Electronic digital computers.
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