Recent advances in machine learning (ML) have depended on the continued progress of hardware computing platforms. Future advances will depend even more on the synergistic progress of hardware and software. This is the case particularly for embedded ML applications, where developers must meet performance requirements under tighter resource constraints. The emerging open-source hardware community can play a unique role in supporting embedded ML research. ESP is an open-source research platform to design and program heterogeneous systems-on-chip. With the design automation capabilities of ESP, application developers can synthesize hardware accelerators from models specified in common ML frameworks, integrate these accelerators in a complete system-on-chip, and quickly obtain FPGA-based prototypes to evaluate their design by running embedded ML applications.
