Abstract
Adaptive noise data filtering in real-time requires dedicated hardware to meet demanding time requirements. Both DSP processors and FPGAs were studied with respect to their performance in power consumption, hardware architecture, and speed for real time applications. For testing purposes, real time adaptive noise filters have been implemented and simulated on two different platforms, Motorola DSP56303 EVM and Xilinx Spartan III boards. This study has shown that in high speed applications, FPGAs are advantageous over DSPs with respect of their speed and noise reduction because of their parallel architecture. FPGAs can handle more processes at the same time when compared to DSPs, while the later can only handle a limited number of parallel instructions at a time. The speed in both processors impacts the noise reduction in real time. As the DSP core gets slower, the noise removal in real time gets harder to achieve. With respect to power, DSPs are advantageous over FPGAs. FPGAs have reconfigurable gate structure which consumes more power. In case of DSPs, the hardware has been already configured, which requires less power consumption? FPGAs are built for general purposes, and their silicon area in the core is bigger than that of DSPs. This is another factor that affects power consumption. As a result, in high frequency applications, FPGAs are advantageous as compared to DSPs. In low frequency applications, DSPs and FPGAs both satisfy the requirements for noise cancelling. For low frequency applications, DSPs are advantageous in their power consumption and applications for the battery power devices. Software utilizing Matlab, VHDL code run on Xilinix system, and assembly running on Motorola development systems, have been used for the demonstration of this study.
Highlights
The performance of real-time data processing is often limited to the processing capability of the system
Literature survey has showed that high-end Field Programmable Gate Arrays (FPGA) have a huge throughput advantage over high performance Digital Signal processors (DSPs) processors for certain types of signal processing applications
DSP processors are highly efficient for common DSP tasks, but the DSP typically takes only a tiny fraction of the silicon area, which is dedicated for computation purposes
Summary
The performance of real-time data processing is often limited to the processing capability of the system. There have been many discussions regarding the preference of Digital Signal processors (DSPs) or Field Programmable Gate Arrays (FPGA) in real time noise cancellation. Signals from the real world received in analog form, discretely sampled for a digital computer to understand and manipulate. Functionality that is not normally predicted at the outset can be uploaded to the satellite when needed. To test the adaptive noise cancelling, the least mean square (LMS) approach has been used. Besides the standard LMS algorithm, the modified algorithms that are proposed by Stefano [1] and by Das [2] have been implemented for the noise cancellation approach, giving the opportunity of comparing both platforms with respect
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