CCF:SHF:Medium: Automated End-to-End Synthesis for Programmable Analog & Mixed-Signal Systems

CCF:SHF:Medium：可编程模拟的自动端到端综合

• 批准号: 2212179
• 负责人: Jennifer Hasler
• 金额: $ 120万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212179&HistoricalAwards=false
• 关键词: CCF; SHF; Medium; Automated; End

Automated digital-design tools have enabled a wide range of digital innovations, and automating mixed-signal and analog design tools have the potential for an equally wide range of electronic innovations. Today’s analog system design processes are impeding the development of new integrated circuits (ICs) processing due to the required manual effort. The emerging demand for low-power high-efficiency analog and mixed-signal-computing (e.g. machine learning) further is only increasing the bottlenecks imposed by the state of the art in analog system design. The success of this project would drastically decrease the design time, design cost, and design uncertainty of large-scale analog and mixed-signal ICs. These innovations will require educational innovation at multiple levels and could enable future commercial opportunities built around these tools. This project proposes a design-automation framework that enables end-to-end synthesis from the high-level description down to targeted configurable devices or custom silicon IC layouts. It lays a solid technology-independent foundation to abstract and automate analog design. This tool capability enables a new vision for analog and mixed-signal computing, similar to that has already seen in digital design spaces. This effort creases the first analog/mixed-signal high-level synthesis, i.e., from behavioral-level description such as Python, as well as text-based analog/mixed-signal mid-level synthesis. The co-design of mixed analog/digital architectures enables real-time to near-zero latency computing for ultra-low-energy applications, while expanding the architectural design space for analog circuits.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自动化数字设计工具已经实现了广泛的数字创新，而自动化混合信号和模拟设计工具也有可能实现同样广泛的电子创新。 当今的模拟系统设计过程由于所需的人工工作而阻碍了新的集成电路（IC）处理的开发。 对低功耗高效率模拟和混合信号计算（例如机器学习）的新兴需求进一步增加了模拟系统设计中现有技术所带来的瓶颈。 该项目的成功将大大减少大规模模拟和混合信号IC的设计时间、设计成本和设计不确定性。 这些创新将需要在多个层面上进行教育创新，并可以围绕这些工具创造未来的商业机会。该项目提出了一个设计自动化框架，可以实现从高级描述到目标可配置器件或自定义硅IC布局的端到端综合。 它为抽象和自动化模拟设计奠定了坚实的技术无关的基础。该工具功能为模拟和混合信号计算带来了新的愿景，类似于数字设计领域中已经出现的愿景。这一努力增加了第一个模拟/混合信号高级合成，即，从行为级描述（如Python），以及基于文本的模拟/混合信号中级合成。 混合模拟/数字架构的协同设计使超低能耗应用的实时计算接近于零延迟，同时扩展了模拟电路的架构设计空间。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响评审标准进行评估，被认为值得支持。