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The current trend for domain-specific architectures (DSAs) has led to renewed interest in research test chips to demonstrate new specialized hardware. Tape-outs also offer huge pedagogical value garnered from real hands-on exposure to the whole system stack. However, successful tape-outs demand hard-earned experience, and the design process is time consuming and fraught with challenges. Therefore, custom chips have remained the preserve of a small number of research groups, typically focused on circuit design research. This paper describes the CHIPKIT framework. We describe a reusable SoC subsystem which provides basic IO, an on-chip programmable host, memory and peripherals. This subsystem can be readily extended with new IP blocks to generate custom test chips. We also present an agile RTL development flow, including a code generation tool calledVGEN. Finally, we outline best practices for full-chip validation across the entire design cycle.
ESP is an open-source research platform for heterogeneous SoC design. The platform combines a modular tile-based architecture with a variety of application-oriented flows for the design and optimization of accelerators. The ESP architecture is highly
Continuous scaling of the VLSI system leaves a great challenge on manufacturing and optical proximity correction (OPC) is widely applied in conventional design flow for manufacturability optimization. Traditional techniques conducted OPC by leveragin
We introduce ratatoskr, an open-source framework for in-depth power, performance and area (PPA) analysis in NoCs for 3D-integrated and heterogeneous System-on-Chips (SoCs). It covers all layers of abstraction by providing a NoC hardware implementatio
Manycore System-on-Chip include an increasing amount of processing elements and have become an important research topic for improvements of both hardware and software. While research can be conducted using system simulators, prototyping requires a va
Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (