License single eBooks
Your results
Search and Filter
Free text
Search across title, subtitle, authors, and ISBNs
ISBNs
Copy and paste up to 1000 ISBNs separated by comma(,), space (‘ ‘), or line break
Loading...
United Nations Sustainable Development Goals (SDGs)
Top books for working towards the SDGs, hand-selected by Springer Nature editors
0 selected out of 0 items
Series
Lecture notes, etc. How to find other series
0 selected out of 0 items
Copyright years
From 1815 onwards
0 selected out of 0 items
Collections
Our most popular sets of eBooks
R0 collections
Reference modules
0 selected out of 0 items
Subject
SN’s own taxonomy
Loading...
0 selected out of 0 items
-
For High Performance Computing, Deep Neural Networks and Data ScienceJunichiro Makino978-3-030-76871-32021 Edition 1
- This book gives a new view on how the processors for HPC, AI and Data Science should be designed. Traditional approaches are, even when called 'quantitative', evolutionary, in the sense that the starting point is the existing software optimized to existing processors. In this book, first the applications are classified into several categories, so that their requirements can be summarized. Then the concept of the efficiency is introduced as the guiding principle for the processor design
- The efficiency is simply defined as the fraction of electricity (and also silicon die area) used in the combinatorial logics for arithmetic operations. In many of modern processors, efficiencies in this sense are surprisingly low, implying that there is huge room of improvements. Also, writing application software for these modern processors has become very difficult
- In this book, examples of designs with very high efficiency are presented, with the overview on how the application software can be developed, based on the author's experience on the development of SIMD parallel processors
€320