Polarization fingerprint (PF) is a promising technique for low-cost Internet of Things (IoT) device identification, which has better performance compared to radio frequency fingerprint (RFF). However, due to the manufacturing technology improvement, PF and RFF suffer from critical drawbacks, especially the reduction in fingerprint differences among devices. To solve this problem, we propose an artificial polarization fingerprint injection (APFI) scheme based on convenient antenna surface slotting. First, we find slotting can produce frequencydependent variation in polarization, which increases the fingerprint difference. Subsequently, we proposed the polarization frequency gradient matrix as a classification feature, based on the characteristics of the injected fingerprints, which eliminates the spatial instability of the original PFs. Extensive simulations and experiments demonstrate that APFI improves the device capability more than 7 times compared with the original PFs under 99.5% identification accuracy.

Currently, radio frequency fingerprint (RFF), which describes the physical layer features of wireless signals in time-frequency domain, has been intensively studied. However, the research of polarization fingerprint (PF), which describes the physical layer features of wireless signals in the polarization domain, has just started. In this paper, PF is deeply studied, and a PF-based LoRaWAN physical layer authentication solution is designed under the actual application scenario. Firstly, the physical layer features in polarization are analyzed from the full path of polarization formation, propagation and reception, and the mathematical model of PF is constructed. There exist two main properties in PF: frequency characteristic and spatial characteristic. The spatial characteristic is the unique property of PF compared with RFF, which significantly improves the fingerprint discrimination of the same type of device. Subsequently, the solution has two authentication mechanisms according to different deployment strategies of LoRa devices: polarization fingerprint identification (PFI) based authentication and PF tracking-based authentication. PFI is implemented based on ensemble convolutional neural network (CNN), which assigns different weights to parts of PF with different discrimination to improve identification accuracy. PF tracking-based authentication solves the impact of LoRa device movement on authentication. Finally, the authentication performance and robustness of the solution are evaluated by experiments, and the ability of the solution to deal with typical attacks is analyzed. In summary, the PF-based LoRaWAN physical layer authentication solution provides considerable application potential.

We present an approach to the automatic generation of efficient matrix multiplication code on the latest Sunway processor, which will be employed by the next-generation machine of Sunway TaihuLight, one of the fastest supercomputers on earth. The method allows users to write simple C code and automatically generates high-performance matrix multiplication kernels. It uses polyhedral transformations to implement rapid compute decomposition, data exchanges across memory hierarchy and memory latency hiding. An assembly routine is finally integrated into the generated kernels. While achieving up to 90.14% of the theoretical peak performance, our method surpasses a highly tuned library by 9.44%. Compared with existing techniques, our approach reduces the software development life cycle to generate efficient matrix code from months to seconds. We also take into account batched matrix multiplication and some fusion patterns for deep learning (DL), outperforming the library-based implementations by 1.30 × and 1.67 ×.

Radio frequency fingerprint (RFF) is a mature physical-layer identification technique for specific emitter identifi-cation (SEI). However, RFF faces the problem of low discrepancy. Therefore we propose polarization fingerprint (PF). PF has frequency characteristics derived from antenna hardware im-perfections and spatial characteristics derived from the vectorial properties of polarization. Based on these two characteristics, the mathematical model of PF is constructed. Because the spatial characteristics are not limited by the improvement of hardware manufacturing process, it still has a significant discrepancy. We also propose a spatial polarization compensation (SPC) for PF changes due to emitter movement. With spatial characteristics and SPC, PF can be effectively applied to SEI. However, the spatial characteristics also bring a trade-off between the identi-fication accuracy and the maximum emitter capacity. Finally, these two characteristics of PF and the proposed SPC are experimentally verified, then the identification accuracy of the proposed method is measured in real scenarios.

Radio frequency (RF) fingerprinting is a low-cost, high-efficiency, and high-security authentication technique for wireless IoT devices with limited resources, but RF fingerprinting faces problems such as small fingerprint differences, low finger-print stability, and high implementation difficulty. In order to solve these problems, we propose a novel concept of polarization fingerprinting. Polarization fingerprint (PF) is manifested as the correlation between polarization state and frequency. The properties of PF include group feature, individual feature and directionality. Group feature characterizes the antenna structure, and individual feature characterizes the antenna hardware imperfections. The directionality comes from the vector property of polarization and contains the relative position information of the communicating devices. The directionality solves the problem of similar fingerprints that may occur when the number of devices increases, which cannot be solved by RF fingerprinting. Compared to RF fingerprint, PF can exist stably and continuously, which not only solves the problem of low fingerprint stability, but also makes polarization fingerprinting based authentication easier to be implemented. The stability and continuity of PF allow more samples to be obtained during authentication. We also proved that increasing the sample amount can reduce the false alarm rate of authentication. Finally, we conducted experiments based on wireless IoT devices. Experiment results show that polarization fingerprinting based authentication has better performance than RF fingerprinting based under the same conditions.

Abstract The heterogeneous many-core architecture plays an important role in the fields of high-performance computing and scientific computing. It uses accelerator cores with on-chip memories to improve performance and reduce energy consumption. Scratchpad memory (SPM) is a kind of fast on-chip memory with lower energy consumption compared with a hardware cache. However, data transfer between SPM and off-chip memory can be managed only by a programmer or compiler. In this paper, we propose a compiler-directed multithreaded SPM data transfer model (MSDTM) to optimize the process of data transfer in a heterogeneous many-core architecture. We use compile-time analysis to classify data accesses, check dependences and determine the allocation of data transfer operations. We further present the data transfer performance model to derive the optimal granularity of data transfer and select the most profitable data transfer strategy. We implement the proposed MSDTM on the GCC complier and evaluate it on Sunway TaihuLight with selected test cases from benchmarks and scientific computing applications. The experimental result shows that the proposed MSDTM improves the application execution time by 5.49 $$\times$$ × and achieves an energy saving of 5.16 $$\times$$ × on average.

The difference between vector machine and SIMD extension is analyzed at the very start. The multilevel loop vector code generation algorithm termed Codegen put forward by Kennedy and other fellows can't be directly applied to SIMD extension as it is oriented to vector machine. The vectorization algorithm in state-of-the-art compilers can only process one level of nested loop. In order to vectorize the entire nested loop, a vectorization algorithm based on strip mining called simdcodegen is proposed. Firstly, the formation reason of dependence circles is analyzed and the role of strip mining played in elimination of dependence circles is discussed. Then on the basis of codegen, strip mining is applied on each level of the loop recursively to explore the SIMD parallelism in the nested loop. Although strip mining is always legitimate, the executing cost increases after strip mining. To assure that strip mining is beneficial, that is to say, strip mining can break some dependence circles, cycle broken test is applied before implementation of strip mining. Effectiveness of this method is verified by the experimental results.