# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 1 Introduction  
Synthetic aperture radar (SAR) is an active radar system that employs radio waves to generate high-resolution images of large areas, such as land or ocean surfaces. It has applications in various fields, including military and civilian domains [1, 2]. Achieving high performance in radar systems depends on obtaining high-quality images [3, 4]. A high azimuth resolution is a prerequisite for obtaining such images. Although the azimuth resolution typically increases with the antenna size, there are limits to enlarging antennas. However, SAR systems typically mount radar on mobile platforms such as aircraft. As the platform moves, the radar continuously acquires data, enabling the generation of high-resolution images through pulse compression processes in both the range and azimuth directions. Therefore, SAR systems can achieve high resolution using small antennas [5, 6]. Optical sensors are widely used in remote sensing and primarily detect objects using visible, infrared, and ultraviolet light. Examples include cameras, telescopes, and infrared cameras. However, optical sensors may be sensitive to adverse weather conditions such as fog, rain, snow, and clouds, leading to performance degradation. Additionally, insufficient light at night may hinder image generation. In contrast, SAR employs electromagneticwaves to detect the range and velocity of the objects. Therefore, unaffected by visual features or lighting conditions, SAR can produce high-quality images regardless of the time of day or prevailing weather conditions [7,8]. These advantages render SAR particularly valuable for surveillance and reconnaissance applications. In these fields, real-time image acquisition and rapid response are crucial [9]. However, real-time SAR image generation requires complex signal processing operations, which pose a challenge owing to the large volume of raw data typically involved. Algorithms for efficiently generating SAR images include the range-Doppler algorithm (RDA) [10, 11, 12, 13, 14], chirp scaling algorithm (CSA) [15, 16, 17], polar format algorithm (PFA) [18, 19, 20], and back-projection algorithm (BPA) [21, 22, 23]. Among these, the RDA, which has the most intuitive physical concepts and the best trade-off between image quality and computational efficiency, is the most widely used. The RDA mainly comprises a range compression process, a range cell migration correction (RCMC) process, and an azimuth compression process. Because both range and azimuth compression processes primarily operate in the frequency domain, they involve fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) operations. The RDA requires a long execution time because several FFT and IFFT operations are repeatedly performed on two-dimensional (2D) images. These operations are crucial for accurately reconstructing the amplitude and phase of the signals reflected from each point on the ground, which is essential for high-resolution image generation. However, processing a large volume of raw data with the low data reuse rate inherent in FFT operations necessitates a significant number of memory requests. Additionally, FFT and IFFT operations demand high memory bandwidth, leading to memory bottlenecks that not only slow down execution but also contribute to significant energy consumption. Given that SAR systems must handle large volumes of data to generate high-resolution ground images, optimizing both the performance and energy efficiency of these operations is critical. In particular, the numerous memory accesses required by multidimensional Fourier transforms, a key part of SAR signal processing, pose a substantial challenge. Each memory access contributes to the overall energy consumption, which can be considerable given the volume of data processed. Therefore, in addition to accelerating the FFT in the RDA, optimizing the energy efficiency of these memory-intensive processes is equally important.