Artificial Intelligence Hardware Design Challenges and Solutions

The authors offer readers an illustration of in-memory computation through Georgia Tech's Neurocube and Stanford's Tetris accelerator using the Hybrid Memory Cube, as well as near-memory architecture through the embedded eDRAM of the Institute of Computing Technology, the Chinese Academy of Science, and other institutions.
Readers will also find a discussion of 3D neural processing techniques to support multiple layer neural networks, as well as information like:

• A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models
  
• Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement
  
• Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU
  
• An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition
  

Perfect for hardware and software engineers and firmware developers,Artificial Intelligence Hardware Designis an indispensable resource for anyone working with Neural Processing Units in either a hardware or software capacity.