With the end of Moore's Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application. DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. In the emerging field of big data, machine learning, data mining, and artificial intelligence algorithms, as the core components of next-generation applications, have attracted more attention from researchers. Utilizing existing hardware and software means to carry out the design of a new algorithmic architecture has become a hot research topic nowadays. Accelerating new algorithms in the era of big data is very different from the past. Machine Learning is concerned with using data to construct appropriate predictive models to make predictions about unknown data. According to the similarity of the presentation and implementation of Machine Learning algorithms, we can categorize the algorithms such as Bayesian-based algorithms and neural network-based algorithms. Of course, the scope of machine learning is so vast that some algorithms are difficult to categorize explicitly into a particular class, and for some classifications, algorithms of the same classification can target different types of problems.