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CHIPYARD

Chipyard Framework Test

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Using Chipyard

To get started using Chipyard, see the documentation on the Chipyard documentation site: https://chipyard.readthedocs.io/

What is Chipyard

Chipyard is an open source framework for agile development of Chisel-based systems-on-chip. It will allow you to leverage the Chisel HDL, Rocket Chip SoC generator, and other Berkeley projects to produce a RISC-V SoC with everything from MMIO-mapped peripherals to custom accelerators. Chipyard contains processor cores (Rocket, BOOM, CVA6 (Ariane)), vector units (Saturn, Ara), accelerators (Gemmini, NVDLA), memory systems, and additional peripherals and tooling to help create a full featured SoC. Chipyard supports multiple concurrent flows of agile hardware development, including software RTL simulation, FPGA-accelerated simulation (FireSim), automated VLSI flows (Hammer), and software workload generation for bare-metal and Linux-based systems (FireMarshal). Chipyard is actively developed in the Berkeley Architecture Research Group in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley.

Resources

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Contributing

Attribution and Chipyard-related Publications

If used for research, please cite Chipyard by the following publication:

@article{chipyard,
  author={Amid, Alon and Biancolin, David and Gonzalez, Abraham and Grubb, Daniel and Karandikar, Sagar and Liew, Harrison and Magyar,   Albert and Mao, Howard and Ou, Albert and Pemberton, Nathan and Rigge, Paul and Schmidt, Colin and Wright, John and Zhao, Jerry and Shao, Yakun Sophia and Asanovi\'{c}, Krste and Nikoli\'{c}, Borivoje},
  journal={IEEE Micro},
  title={Chipyard: Integrated Design, Simulation, and Implementation Framework for Custom SoCs},
  year={2020},
  volume={40},
  number={4},
  pages={10-21},
  doi={10.1109/MM.2020.2996616},
  ISSN={1937-4143},
}

These additional publications cover many of the internal components used in Chipyard. However, for the most up-to-date details, users should refer to the Chipyard docs.

Acknowledgements

This work is supported by the NSF CCRI ENS Chipyard Award #2016662.