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Emerging technology and architecture for big-data analytics
Chattopadhyay A., Chang C., Yu H., Springer International Publishing, New York, NY, 2017. 330 pp. Type: Book (978-3-319548-39-5)
Date Reviewed: Feb 22 2018

Readers will always warmly welcome a book on a hot topic in contemporary computer science, and big data is such a topic. As can be seen by looking at the book, the topic is also an important issue for hardware designers and low-level software specialists. The book is not devoted to what can be at first glance associated with big data, that is, algorithm design; rather, the focus is on the supporting electronics. (Obviously, various algorithms are present in the book as well.)

This book is a collection of independent texts--the common factor is that the covered approaches are or can be somehow related to processing huge amounts of data. The following topics are covered: in Part 1, a performance analysis of Java Virtual Machine on many-core enterprise servers (chapter 1), a proposal of an implementation of data analytic kernels on field programmable gate arrays (FPGAs) (chapter 2), an FPGA-based realization of a least-square-solver-based machine learning accelerator (chapter 3), a proposal of a hardware acceleration architecture for data-intensive kernels (chapter 4), and a proposal of an original cross-layer design methodology to improve electronic design automation for possible support of the Internet of Things (chapter 5). The remaining two parts are more application-oriented, while the hardware context is still present. However, the division of the parts is rather loose. Part 2 deals with different applications: a proposal of how to resist side channel attacks in residue number systems by using so-called leakage-resistant arithmetic (chapter 6), a proposal of a low-energy biomedical circuit (chapter 7), a method to speed up MapReduce operations on multicore processors (chapter 8), and a performance evaluation of various compression strategies for a type of speech processing (chapter 9). Part 3 is mainly data processing oriented: a few chapters are devoted to artificial intelligence concepts, that is, support of the related applications by memristor-based systems (chapter 10), support of spiking neural network applications with resistive random-access memory (RRAM) systems (chapter 11), and support of deep neural networks with spintronic crossbars (chapter 12). The last three chapters deal with somewhat different topics: there is one more example application of RRAM, but this time applied to LZ77-based compression (chapter 13), an approach to the processing of brain signals (chapter 14), and a tutorial on quantum computing.

Although the book does not present a consequent flow of themes resulting in a consistent body of material, it will be useful and inspiring for specialists working on contemporary hardware solutions with specific aims. Unlike many other collections of chapters presenting different topics and written by various authors, in this case the work is recommended as a good survey on what is going on with regard to the topic of hardware issues experienced when processing massive data.

Reviewer:  Piotr Cholda Review #: CR145871 (1805-0181)
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