FARSI：Facebook AR系统调查员，用于敏捷领域特定片上系统探索
FARSI: Facebook AR System Investigator for Agile Domain-Specific System-on-Chip Exploration
摘要：
特定领域SoC（DSSoC）是具有严格功率/性能/面积约束的领域的有吸引力的解决方案;然而，它们遭受两个基本的复杂性。一方面，它们的许多专用硬件模块导致复杂的系统，从而导致高开发工作量。另一方面，它们的许多系统旋钮扩大了设计空间的复杂性，使得寻找最优设计变得困难。因此，要达到普及，驯服这种复杂性是必要的。这项工作确定了早期设计空间探索（DSE）框架的必要特征，该框架针对DSSoCs的复杂设计空间，并进一步提供了一个名为FARSI的实例，（F）acebook（AR）（S）approximate（I）nvestigator。具体来说，FARSI提供了一个敏捷的系统级仿真器，与Synopsys Platform Architect相比，其速度和准确度分别为8,400倍和98.5%。FARSI还提供了一个有效的探索启发式，并实现了高达16倍的改进收敛时间相比，天真的模拟退火（SA）。这是通过增强SA与建筑推理，如局部开发和瓶颈放松。此外，我们嵌入了各种协同设计功能，并表明平均而言，它们对收敛速度有32%的影响。最后，我们证明了使用简单的开发成本感知策略可以降低系统复杂性，无论是在组件数量和变化方面，都可以降低150%和118%（例如，用于片上网络子系统）
英文摘要：
Domain-specific SoCs (DSSoCs) are attractive solutions for domains with stringent power/performance/area constraints; however, they suffer from two fundamental complexities. On the one hand, their many specialized hardware blocks result in complex systems and thus high development effort. On the other, their many system knobs expand the complexity of design space, making the search for the optimal design difficult. Thus to reach prevalence, taming such complexities is necessary. This work identifies necessary features of an early-stage design space exploration (DSE) framework that targets the complex design space of DSSoCs and further provides an instance of one called FARSI, (F)acebook (AR) (S)ystem (I)nvestigator. Concretely, FARSI provides an agile system-level simulator with speed up and accuracy of 8,400X and 98.5% comparing to Synopsys Platform Architect. FARSI also provides an efficient exploration heuristic and achieves up to 16X improvementin convergence time comparing to naive simulated annealing (SA). This is done by augmenting SA with architectural reasoning such as locality exploitation and bottleneck relaxation. Furthermore, we embed various co-design capabilities and show that on average, they have a 32% impact on the convergence rate. Finally, we demonstrate that using simple development-cost-aware policies can lower the system complexity, both in terms of the component count and variation by as much as 150% and 118% (e,g., for Network-on-a-Chip subsystem)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
