A Comparison of Single-Buffer and Double-Buffer Design in a Systolic Array Generator

Abstract In application fields such as face recognition and image recognition using deep learning, more convolution operations are required for the increasing amount of data. Therefore, the use of systolic array acceleration is becoming a key technology trend to accelerate the development of deep learning applications. In previous designs, most of the systolic array used single-buffer or double-buffer structures, but most of them did not compare the difference between the two in detail. This work designs and implements a three-level systolic array generator, which can be configured in single-buffer or double-buffer modes. We also program the generated systolic array accelerator on Nexys-Vedio FPGA to explore the performance and overhead of single-buffer and double-buffer structure modes. The results show that the throughput of the double-buffer structure is increased by nearly 3 × compared to the single-buffer structure while only brings an additional 28% power consumption and 25% area overhead under the SIMC 130nm technology. And compared with the previous work, the proposed systolic array generator reduces power consumption by 75% and area overhead by 34% with almost no loss in performance.</jats:p

Clutter Suppression Algorithm with Joint Intrinsic Clutter Motion Errors Calibration and Off-Grid Effects Mitigation in Airborne Passive Radars

In an airborne passive radar, multipath (MP) clutter, which is caused by MP signals contained in the contaminated reference signal, degrades the space-time adaptive processing (STAP) performance. The MP clutter suppression algorithm before STAP can mitigate the influence of impure reference signals. However, the performances of the existing MP clutter suppression methods deteriorate when the intrinsic clutter motion (ICM) exists because the sparse model of MP clutter is disturbed. To eliminate the impacts of ICM on MP clutter suppression, a joint optimization algorithm is developed for airborne passive radar. Firstly, the sparse model of MP clutter is modified by taking ICM fluctuation into account. Subsequently, the joint optimization function of the ICM fluctuation and MP clutter profile is derived. Finally, based on the local search technique, MP clutter is suppressed with ICM error calibration and off-grid effects mitigation. A range of simulations verify the reliability and superiority of the proposed method

Intelligent Security Monitoring System Based on RISC-V SoC

With the development of the economy and society, the demand for social security and stability increases. However, traditional security systems rely too much on human resources and are affected by uncontrollable community security factors. An intelligent security monitoring system can overcome the limitations of traditional systems and save human resources, contributing to public security. To build this system, a RISC-V SoC is first designed in this paper and implemented on the Nexys-Video Artix-7 FPGA. Then, the Linux operating system is transplanted and successfully run. Meanwhile, the driver of related hardware devices is designed independently. After that, three OpenCV-based object detection models including YOLO (You Only Look Once), Haar (Haar-like features), and LBP (Local Binary Pattern) are compared, and the LBP model is chosen to design applications. Finally, the processing speed of 1.25 s per frame is realized to detect and track moving objects. To sum up, we build an intelligent security monitoring system with real-time detection, tracking, and identification functions through hardware and software collaborative design. This paper also proposes a video downsampling technique. Based on this technique, the BRAM resource usage on the hardware side is reduced by 50% and the amount of pixel data that needs to be processed on the software side is reduced by 75%. A video downsampling technology is also proposed in this paper to achieve better video display effects under limited hardware resources. It provides conditions for future function expansion and improves the models’ processing speed. Additionally, it reduces the run time of the application and improves the system performance

Intelligent Security Monitoring System Based on RISC-V SoC

With the development of the economy and society, the demand for social security and stability increases. However, traditional security systems rely too much on human resources and are affected by uncontrollable community security factors. An intelligent security monitoring system can overcome the limitations of traditional systems and save human resources, contributing to public security. To build this system, a RISC-V SoC is first designed in this paper and implemented on the Nexys-Video Artix-7 FPGA. Then, the Linux operating system is transplanted and successfully run. Meanwhile, the driver of related hardware devices is designed independently. After that, three OpenCV-based object detection models including YOLO (You Only Look Once), Haar (Haar-like features), and LBP (Local Binary Pattern) are compared, and the LBP model is chosen to design applications. Finally, the processing speed of 1.25 s per frame is realized to detect and track moving objects. To sum up, we build an intelligent security monitoring system with real-time detection, tracking, and identification functions through hardware and software collaborative design. This paper also proposes a video downsampling technique. Based on this technique, the BRAM resource usage on the hardware side is reduced by 50% and the amount of pixel data that needs to be processed on the software side is reduced by 75%. A video downsampling technology is also proposed in this paper to achieve better video display effects under limited hardware resources. It provides conditions for future function expansion and improves the models’ processing speed. Additionally, it reduces the run time of the application and improves the system performance.</jats:p

Factors affecting soil microbial biomass and functional diversity with the application of organic amendments in three contrasting cropland soils during a field experiment.

The effects of soil type and organic material quality on the microbial biomass and functional diversity of cropland soils were studied in a transplant experiment in the same climate during a 1-year field experiment. Six organic materials (WS: wheat straw, CS: corn straw, WR: wheat root, CR: corn root, PM: pig manure, CM: cattle manure), and three contrasting soils (Ferralic Cambisol, Calcaric Cambisol and Luvic Phaeozem) were chosen. At two time points (at the end of the 1st and 12th months), soil microbial biomass carbon (C) and nitrogen (N) (MBC and MBN) and Biolog Ecoplate substrate use patterns were determined, and the average well color development and the microbial functional diversity indices (Shannon, Simpson and McIntosh indices) were calculated. Organic material quality explained 29.5-50.9% of the variance in MBC and MBN when compared with the minor role of soil type (1.4-9.3%) at the end of the 1st and 12th months, and C/N ratio and total N of organic material were the main parameters. Soil properties, e.g., organic C and clay content were the predominant influence on microbial functional diversity in particular at the end of the 12th month (61.8-82.8% of the variance explained). The treatments of WS and CS significantly improved the MBC and microbial functional diversity indices over the control in the three soils in both sampling periods (P < 0.05). These results suggest that the application of crop straw is a long-term effective measure to increase microbial biomass, and can further induce the changes of soil properties to regulate soil microbial community