Weed control is a critical challenge in modern agriculture, as weeds compete with crops for essential nutrient resources, significantly reducing crop yield and quality. Traditional weed control methods, including chemical and mechanical approaches, have real-life limitations such as associated environmental impact and efficiency. An emerging yet effective approach is laser weeding, which uses a laser beam as the stem cutter. Although there have been studies that use deep learning in weed recognition, its application in intelligent laser weeding still requires a comprehensive understanding. Thus, this study serves the first empirical study on weed recognition for laser weeding. To increase the efficiency of laser beam cut and avoid damaging the crops of interest, the laser beam shall be directly aimed at the weed root. Yet, weed stem detection remains an under-explored problem. We integrate the detection of crop and weed with the localization of weed stem into one end-to-end system. To train and validate the proposed system in a real-life scenario, we curate and construct a high-quality weed stem detection dataset with human annotations. The dataset consists of 7,161 high-resolution pictures collected in the field with annotations of 11,151 instances of weed. The dataset will be released upon acceptance. Experimental results show that, in contrast to seminal weed recognition systems, the proposed system can efficiently improve the weeding accuracy by 5.05% and reduce the energy cost by 32.3%.

Due to complex abstractions implemented over shared data structures protected by locks, conventional symmetric multithreaded operating system kernel such as Linux is hard to achieve high scalability on the emerging multi-core architectures, which integrate more and more cores on a single die. This paper presents GenerOS - a general asymmetric operating system kernel for multi-core systems. In principal, GenerOS partitions processing cores into application core, kernel core and interrupt core, each of which is dedicated to a specified function. In implementation, we conduct a delicate modification to Linux kernel and provide the same interface as Linux kernel so that GenerOS is compatible with legacy applications. The better performance of GenerOS mainly benefits from: (1) Applications run on their own cores with minimal interrupt and kernel support; (2) Every kernel service is encapsulated in to a serial process so that there will be fewer contentions than conventional symmetric kernel; (3) A slim schedule policy is used in the kernel core to support schedule between system calls with low overhead. Experiments with two typical workloads on 16-core AMD machine show that GenerOS behaves better than original Linux kernel when there are more processing cores (19.6% for TPC-H using oracle database management system and 42.8% for httperf using apache web server).

The quickly development of the multi-core technology brings plenty of logical processors to the symmetric multiprocessing (SMP) system. As all of cores share the same system bus and memory bandwidth, the additional computing resources canpsilat fully play their roles. It is the basic restrict to the scalability of such a system. Furthermore, the operating system which runs in this system typically provides complex abstractions implemented over shared data structures protected by locks. More contentions come along with the increase of cores in such type of kernel. After several detailed experiments to 5 different types of benchmarks, we recognize these problems in the multi-core SMP system. At last, reasons causing the problems are analyzed and corresponding solutions are raised briefly.

Memory trace analysis is an important technology for architecture research, system software (i.e., OS, compiler) optimization, and application performance improvements. Many approaches have been used to track memory trace, such as simulation, binary instrumentation and hardware snooping. However, they usually have limitations of time, accuracy and capacity.In this paper we propose a platform independent memory trace monitoring system, which is able to track virtual memory reference trace of full systems (including OS, VMMs, libraries, and applications). The system adopts a DIMM-snooping mechanism that uses hardware boards plugged in DIMM slots to snoop. There are several advantages in this approach, such as fast, complete, undistorted, and portable. Three key techniques are proposed to address the system design challenges with this mechanism: (1) To keep up with memory speeds, the DDR protocol state machine is simplified, and large FIFOs are added between the state machine and the trace transmitting logic to handle burst memory accesses; (2) To reconstruct physical-tovirtual mapping and distinguish one process' address space from others, an OS kernel module, which collects page table information, and a synchronization mechanism, which synchronizes the page table information with the memory race, are developed; (3) To dump massive trace data, we employ a straightforward method to compress the trace and use Gigabit Ethernet and RAID to send and receive the compressed trace.We present our implementation of an initial monitoring system, named HMTT (Hyper Memory Trace Tracker). Using HMTT, we have observed that burst bandwidth utilization is much larger than average bandwidth utilization, by up to 5X in desktop applications. We have also confirmed that the stream memory accesses of many applications contribute even more than 40% of L2 Cache misses and OS virtual memory management may decrease stream accesses in view of memory controller (or L2 Cache), by up to 30.2%. Moreover, we have evaluated OS impact on memory performance in real systems. The evaluations and case studies show the feasibility and effectiveness of our proposed monitoring mechanism and techniques.