Thinking Machines: Machine Learning and Its Hardware Implementationcovers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the latest trends and examples of machine learning hardware and basic knowledge of machine learning in general, but also the main issues involved in its implementation. Readers will learn what is required for the design of machine learning hardware for neuromorphic computing and/or neural networks. This is a recommended book for those who have basic knowledge of machine learning or those who want to learn more about the current trends of machine learning. Presents a clear understanding of various available machine learning hardware accelerator solutions that can be applied to selected machine learning algorithmsOffers key insights into the development of hardware, from algorithms, software, logic circuits, to hardware acceleratorsIntroduces the baseline characteristics of deep neural network models that should be treated by hardware as wellPresents readers with a thorough review of past research and products, explaining how to design through ASIC and FPGA approaches for target machine learning modelsSurveys current trends and models in neuromorphic computing and neural network hardware architecturesOutlines the strategy for advanced hardware development through the example of deep learning accelerators | Thinking Machines by Shigeyuki Takano, Paperback | Indigo Chapters