The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications.

• Part I: Introduction
• Chapter 1: Introduction
• Chapter 2: The need of in-memory computing
• Chapter 3: The background of ReRAM devices
• Part II: Machine learning accelerators
• Chapter 4: The background of machine learning algorithms
• Chapter 5: XIMA: the in-ReRAM machine learning architecture
• Chapter 6: The mapping of machine learning algorithms on XIMA
• Part III: Case studies
• Chapter 7: Large-scale case study: accelerator for ResNet
• Chapter 8: Large-scale case study: accelerator for compressive sensing
• Chapter 9: Conclusions: wrap-up, open questions and challenges