Ascend AI Processor Architecture and Programming Principles and Applications of CANN
Auteur : Liang Xiaoyao
Ascend AI Processor Architecture and Programming: Principles and Applications of CANN offers in-depth AI applications using Huawei?s Ascend chip, presenting and analyzing the unique performance and attributes of this processor. The title introduces the fundamental theory of AI, the software and hardware architecture of the Ascend AI processor, related tools and programming technology, and typical application cases. It demonstrates internal software and hardware design principles, system tools and programming techniques for the processor, laying out the elements of AI programming technology needed by researchers developing AI applications.
Chapters cover the theoretical fundamentals of AI and deep learning, the state of the industry, including the current state of Neural Network Processors, deep learning frameworks, and a deep learning compilation framework, the hardware architecture of the Ascend AI processor, programming methods and practices for developing the processor, and finally, detailed case studies on data and algorithms for AI.
1. Basic Theory2. Industry Background3. Hardware Architecture4. Software Architecture5. Programming Methods6. Case Studies
- Presents the performance and attributes of the Huawei Ascend AI processor
- Describes the software and hardware architecture of the Ascend processor
- Lays out the elements of AI theory, processor architecture, and AI applications
- Provides detailed case studies on data and algorithms for AI
- Offers insights into processor architecture and programming to spark new AI applications
Date de parution : 07-2020
Ouvrage de 308 p.
19x23.3 cm
Thème d’Ascend AI Processor Architecture and Programming :
Mots-clés :
Accuracy; AI; Application development; Artificial intelligence; Caffe; CNN; Convolutional neural network; CPU; DaVinci architecture; Deep learning; Digital vision preprocessing; DVPP; Energy Efficiency Ratio; Evaluation criteria; FPGA; Framework manager; GPU; Hardware architecture; Huawei ascend AI processor; Image classification; Inference engine; Mean average precision; Mind studio; MindSpore; Model generation; Model migration; Neural network; Object detection; Operator development; Perception; Process orchestration; Programming; Pytorch; Runtime; SoC; Software architecture; System on chip; Task scheduler; TBE; Tensor boost engine; Tensorflow; Throughput and latency; TPU; TVM