Pathmapirian Nanthakumar, Chamith Wijenayake, Chamira U. S. Edussooriya, Arjuna Madanayake, Renato J Cintra
Conference
ISCAS 2025 IEEE International Symposium on Circuits and Systems (ISCAS) , pp. 1-5 · 2025 · Cited by 3
Digital Signal ProcessingFPGA and Reconfigurable ComputingHardware Acceleration and Co-design
Abstract
The combined use of algorithms for approximate discrete cosine transform (DCT) with approximate computing is examined toward area-efficient field programmable gate array (FPGA) hardware architectures. The performance of previously reported, multiplierless approximate DCT transforms are explored in the presence of non-exact hardware adders which provide better utilisation of FPGA resources in terms of look-up-tables (LUTs). In the context of 2D image compression, provided examples are demonstrating a 20% LUT reduction in FPGA utilization compared to accurate adders, with a corresponding image quality degradation of 0.6 dB in terms of PSNR. The potential benefit of such area-efficient FPGA hardware designs is explored on 3D (video compression), 4D (static light field compression), and 5D (light field video compression) cases where significant FPGA resource savings are obtained at the cost of …
Umesha Kumarasiri, Sivakumar Sivasankar, Hasitha Weerasooriya, HRPS De Silva, Chamira U. S. Edussooriya, Viduneth Ariyarathna, Francesco Restuccia, Arjuna Madanayake
Journal
IEEE Journal of Radio Frequency Identification , vol. 9 , pp. 426-438 · 2025 · Cited by 2
Wireless CommunicationsArray and Radar Signal ProcessingFPGA and Reconfigurable Computing
Abstract
Real-time sensing and perception of the radio spectrum based on artificial intelligence (AI) is crucial for emerging intelligent wireless and electronic warfare systems. However, sensing can be greatly impacted by harmful radio frequency interference (RFI). Emerging drone warfare allows many RFI sources/jammers to be distributed across a wide field-of-view which necessitates real-time measurement, adaptation and aperture nulling to remove the RFI before AI-based sensing and perception of sources of interest can occur. This work explores algorithmic innovations that improve the computational complexity of classical Howells-Applebaum adaptive nulling algorithm to enable fast, real-time adaptive operation at significantly lower arithmetic complexity. Design examples for AI-enabled sensing and perception across a 32-element antenna receiver with 32 independent channels and a Xilinx Virtex-6 Sx475 FPGA …