target="_blank" rel="nofollow" href="#u0ef354ac-d0a1-5a34-8fa5-60421c56219e">Chapter 3 lists out several parallel architectures, Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU. It emphasizes hardware/software integration for performance improvement. Nvidia Deep Learning Accelerator (NVDLA) open‐source project is chosen for FPGA hardware implementation.
Chapter 4 introduces a streaming graph for massive parallel computation through Blaize GSP and Graphcore IPU. They apply the Depth First Search (DFS) for task allocation and Bulk Synchronous Parallel Model (BSP) for parallel operations.
Chapter 5 shows how to optimize convolution with the University of California, Los Angeles (UCLA) Deep Convolutional Neural Network (DCNN) accelerator filter decomposition and Massachusetts Institute of Technology (MIT) Eyeriss accelerator Row Stationary dataflow.
Chapter 6 illustrates in‐memory computation through Georgia Institute of Technologies Neurocube and Stanford Tetris accelerator using Hybrid Memory Cube (HMC) as well as University of Bologna Neurostream accelerator using Smart Memory Cubes (SMC).
Chapter 7 highlights near‐memory architecture through the Institute of Computing Technology (ICT), Chinese Academy of Science, DaDianNao supercomputer and University of Toronto Cnvlutin accelerator. It also shows Cnvlutin how to avoid ineffectual zero operations.
Chapter 8 chooses Stanford Energy Efficient Inference Engine, Institute of Computing Technology (ICT), Chinese Academy of Science Cambricon‐X, Massachusetts Institute of Technology (MIT) SCNN processor and Microsoft SeerNet accelerator to handle network sparsity.
Chapter 9 introduces an innovative 3D neural processing with a network bridge to overcome power and thermal challenges. It also solves the memory bottleneck and handles the large neural network processing.
In English edition, several chapters are rewritten with more detailed descriptions. New deep learning hardware architectures are also included. Exercises challenge the reader to solve the problems beyond the scope of this book. The instructional slides are available upon request.
We shall continue to explore different deep learning hardware architectures (i.e. Reinforcement Learning) and work on a in‐memory computing architecture with new high‐speed arithmetic approach. Compared with the Google Brain floating‐point (BFP16) format, the new approach offers a wider dynamic range, higher performance, and less power dissipation. It will be included in a future revision.
Albert Chun Chen Liu
Oscar Ming Kin Law
Acknowledgments
First, we would like to thank all who have supported the publication of the book. We are thankful to Iain Law and Enoch Law for the manuscript preparation and project development. We would like to thank Lincoln Lee and Amelia Leung for reviewing the content. We also thank Claire Chang, Charlene Jin, and Alex Liao for managing the book production and publication. In addition, we are grateful to the readers of the Chinese edition for their valuable feedback on improving the content of this book. Finally, we would like to thank our families for their support throughout the publication of this book.
Albert Chun Chen Liu
Oscar Ming Kin Law
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