Accelerator design is expensive due to the effort required to understand an algorithm and optimize the design. Architects have embraced two technologies to reduce costs. High-level synthesis automatically generates hardware from code. Reconfigurable fabrics instantiate accelerators while avoiding fabrication costs for custom circuits. We further reduce design effort with statistical learning. We build an automated framework, called Prospector, that uses Bayesian techniques to optimize synthesis directives, reducing execution latency and resource usage in field-programmable gate arrays. We show in a certain amount of time that designs discovered by Prospector are closer to Pareto-efficient designs compared to prior approaches. Prospector permits new studies for heterogeneous accelerators.

中文翻译：

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高效加速器合成的贝叶斯优化

由于理解算法和优化设计需要付出努力，加速器设计成本很高。建筑师采用了两种技术来降低成本。高级综合自动从代码生成硬件。可重构结构实例化加速器，同时避免定制电路的制造成本。我们通过统计学习进一步减少设计工作。我们构建了一个名为 Prospector 的自动化框架，该框架使用贝叶斯技术来优化综合指令，减少现场可编程门阵列中的执行延迟和资源使用。我们在一定时间内表明，与以前的方法相比，Prospector 发现的设计更接近帕累托效率设计。Prospector 允许对异构加速器进行新的研究。