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150587812
Synthesis Lectures on Computer Architect Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production, (Paperback)
Item #:
150587812
SAR 251
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This book provides a solid understanding of the design, training, and applications of deep learning algorithms for industry, emphasizing collaboration across various engineering disciplines.
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What Stands Out
Comprehensive Coverage
This book offers an in-depth exploration of algorithms, compilers, and processors tailored for large-scale deep learning systems, making it an essential resource for computer architecture enthusiasts and professionals.
Expert Contributions
Written by leading experts in the field, it provides insights into current trends and breakthroughs, ensuring readers gain access to up-to-date knowledge that's critical for advancing their understanding of deep learning systems.
Practical Applications
Focusing on real-world applications, the text bridges the gap between theoretical concepts and practical implementations, empowering readers to apply learned techniques to optimize deep learning performance in production environments.
Product Details
- This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to bettercollaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | October, 2020 |
| Pages | 245 |
| Subgenre | Computer Architecture |
| Series title | Synthesis Lectures on Computer Architecture |
| Edition | 1 |
| Publisher | Springer International Publishing |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Is collectible | N |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 7.50 x 0.56 x 9.25 in (19.1 x 1.4 x 23.5 cm) |
| Assembled product weight | 1.03 lb (470 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Computer Scientists
Ideal for those researching deep learning algorithms and their integration into computer architecture for real-world applications.
-
Software Engineers
Beneficial for software engineers creating deep learning systems, requiring knowledge of compilers and hardware interactions.
-
Graduate Students
Excellent resource for graduate students specializing in computer architecture or machine learning, offering comprehensive insights.
-
Casual Readers
Not suitable for casual readers seeking general knowledge; the material is technical and requires prior understanding.
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Features & Benefits
- Learn the fundamentals of deep learning systems and algorithms.
- Understand compiler techniques to optimize deep learning code for hardware.
- Explore essential hardware features for today's and future deep learning models.
- Facilitate collaboration among data scientists, hardware designers, and performance engineers.
- Incorporate practical techniques to design and accelerate model performance.
- Access industry insights and examples from leading high-technology companies.
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