Yantır Hasan Erdem, Guo Wenzhe, Eltawil Ahmed M, Kurdahi Fadi J, Salama Khaled Nabil
Sensors Lab, Advanced Membranes & Porous Materials Center (AMPMC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Center for Embedded and Cyber-physical Systems, University of California, Irvine, CA 92697, USA.
Micromachines (Basel). 2019 Jul 31;10(8):509. doi: 10.3390/mi10080509.
Current computation architectures rely on more processor-centric design principles. On the other hand, the inevitable increase in the amount of data that applications need forces researchers to design novel processor architectures that are more data-centric. By following this principle, this study proposes an area-efficient Fast Fourier Transform (FFT) processor through in-memory computing. The proposed architecture occupies the smallest footprint of around 0.1 mm 2 inside its class together with acceptable power efficiency. According to the results, the processor exhibits the highest area efficiency ( FFT / s / area ) among the existing FFT processors in the current literature.
当前的计算架构依赖于更多以处理器为中心的设计原则。另一方面，应用程序所需数据量的必然增加迫使研究人员设计出更以数据为中心的新型处理器架构。遵循这一原则，本研究通过内存计算提出了一种面积高效的快速傅里叶变换（FFT）处理器。所提出的架构在同类产品中占地面积最小，约为0.1平方毫米，同时具有可接受的功率效率。根据结果，该处理器在当前文献中现有的FFT处理器中展现出最高的面积效率（FFT/秒/面积）。
