With the end of Moore's Law and Dennard scaling, the continuous demand for higher computing performance and efficiency is increasingly met through specialization of digital processor implementations. In particular, numerical data processing and machine learning applications incur high computational costs but often have common computational structures, acting as prime targets for hardware customization. Specialization of digital designs is accompanied by substantial non-recurring engineering (NRE) costs, which limit the proliferation of customized designs. This talk presents tools and methodologies for the development of custom systems-on-chip (SoCs) for numerical data analysis applications. An integrated generator-based framework for SoC development is demonstrated through SoC customization and hardware/software co-design for numerical data analysis and machine learning applications. The development of full system support from hardware accelerators through system software leads to the identification of several co-design opportunities for increasing accelerator utility in custom SoCs.