Radio frequency interference (RFI) is a significant challenge faced by today’s radio astronomers. While most past efforts were devoted to cleaning the RFI from the data, we develop a novel method for categorizing and cataloguing RFI for forensic purpose. We present a classifier that categorizes RFI into different types based on features extracted using Principal Component Analysis (PCA) and Fourier analysis. The classifier can identify narrowband non-periodic RFI above 2 σ, narrowband periodic RFI above 3 σ, and wideband impulsive RFI above 5 σ with F1 scores (defined as F1 = (2 · recall × precision)/(recall + precision)) between 0.87 and 0.91 in simulation. This classifier could be used to identify the sources of RFI as well as to clean RFI contamination (particularly in pulsar search). In the long-term analysis of the categorized RFI, we found a special type of drifting periodic RFI that is detrimental to pulsar search. We also found evidences of an increased rate of impulsive RFI when the telescope is pointing toward the cities. These results demonstrate this classifier’s potential as a forensic tool for RFI environment monitoring of radio telescopes.

Radio astronomy uses radio telescopes to detect very faint emissions from celestial objects. However, human-made radio frequency interference (RFI) is currently a common problem faced by most terrestrial radio telescopes, and it is getting worse with the development of the economy and technology. Therefore, it is essential to monitor and evaluate interference during the planning, construction, and operation stages of the radio telescope and protect the quiet radio environment around the radio astronomical site. In this paper, we present a software for an RFI analysis of the radio environment around the telescope. In this software, information has been collected, including the location of the site; the technical specifications, such as aperture and the frequency range of the radio telescopes; and the terrain around the site. The software and its modules are composed of telescope, geographic, and meteorological databases, and analysis modules of terrestrial and space-based RFI. Combined with the propagation characteristics of radio waves, we can analyze and evaluate RFI on the ground and in space around the radio telescope. The feasibility of the software has been proved by the experimental implementation of the propagation properties and RFI source estimation. With this software, efficient technical support can be expected for protecting the radio environment around the telescope, as well as improving site selection for planned radio astronomical facilities.

General matrix multiplication (GEMM) in machine learning involves massive computation and data movement, which restricts its deployment on resource-constrained devices. Although data reuse can reduce data movement during GEMM processing, current approaches fail to fully exploit its potential. This work introduces a sparse GEMM accelerator with a weight-and-output stationary (WOS) dataflow and a distributed buffer architecture. It processes GEMM in a compressed format and eliminates on-chip transfers of both weights and partial sums. Furthermore, to map the compressed GEMM of various sizes onto the accelerator, an adaptable mapping scheme is designed. However, the irregular sparsity of weight matrices makes it difficult to store them in local buffers with the compressed format; denser vectors can exceed the buffer capacity, while sparser vectors may lead to the underutilization of buffers. To address this complication, this work also proposes an offline sparsity-aware shuffle strategy for weights, which balances the utilization of distributed buffers and minimizes buffer waste. Finally, a low-cost sparse computing method is applied to the WOS dataflow with globally shared inputs to achieve high computing throughput. Experiments with an FPGA show that the proposed accelerator achieves 1.73× better computing efficiency and 1.36× higher energy efficiency than existing approaches.