Improving the Performance and Resource Utilization of the CASPER FFT and Polyphase Filterbank

2016 ◽  
Vol 05 (04) ◽  
pp. 1641002
Author(s):  
Ryan Monroe
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Resource Utilization ◽  
Radio Astronomy ◽  
Digital Signal ◽  
Quantization Noise

The effectiveness of Digital Signal Processing (DSP) solutions for radio-astronomy is limited by the efficiency of the implemented algorithms. Novel implementations of several popular DSP algorithms are presented. Their optimization strategies are discussed and their efficiency is compared to that of the standard Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) library solutions. Compared to CASPER, the PFB-FIR and FFT modules require 73% and 45% of the DSP48E1 resources, with performance dominated by ADC quantization noise for typical radio-astronomy inputs.

Approximate Compressor-Based Multiplier Design Methodology for Error-Resilient Digital Signal Processing

2020 ◽  
Vol 29 (14) ◽  
pp. 2050233
Author(s):  
Zhixi Yang ◽  
Xianbin Li ◽  
Jun Yang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Resource Utilization ◽  
Finite Impulse Response ◽  
Digital Signal ◽  
Fir Filter ◽  
Electrical Performance ◽  
Approximate Computing ◽  
Dsp Applications

As many digital signal processing (DSP) applications such as digital filtering are inherently error-tolerant, approximate computing has attracted significant attention. A multiplier is the fundamental component for DSP applications and takes up the most part of the resource utilization, namely power and area. A multiplier consists of partial product arrays (PPAs) and compressors are often used to reduce partial products (PPs) to generate the final product. Approximate computing has been studied as an innovative paradigm for reducing resource utilization for the DSP systems. In this paper, a 4:2 approximate compressor-based multiplier is studied. Approximate 4:2 compressors are designed with a practical design criterion, and an approximate multiplier that uses both truncation and the proposed compressors for PP reduction is subsequently designed. Different levels of truncation and approximate compression combination are studied for accuracy and electrical performance. A practical selection algorithm is then leveraged to identify the optimal combinations for multiplier designs with better performance in terms of both accuracy and electrical performance measurements. Two real case studies are performed, i.e., image processing and a finite impulse response (FIR) filter. The design proposed in this paper has achieved up to 16.96% and 20.81% savings on power and area with an average signal-to-noise ratio (SNR) larger than 25[Formula: see text]dB for image processing; similarly, with a decrease of 0.3[Formula: see text]dB in the output SNR, 12.22% and 30.05% savings on power and area have been achieved for an FIR filter compared to conventional multiplier designs.

scholarly journals Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy

2016 ◽  
Vol 05 (04) ◽  
pp. 1602002 ◽  
Author(s):  
D. C. Price ◽  
J. Kocz ◽  
M. Bailes ◽  
L. J. Greenhill
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Radio Astronomy ◽  
Graphics Processing Units ◽  
High Speed ◽  
High Performance ◽  
Digital Signal ◽  
General Purpose ◽  
Special Issue ◽  
Dsp Systems

Advances in astronomy are intimately linked to advances in digital signal processing (DSP). This special issue is focused upon advances in DSP within radio astronomy. The trend within that community is to use off-the-shelf digital hardware where possible and leverage advances in high performance computing. In particular, graphics processing units (GPUs) and field programmable gate arrays (FPGAs) are being used in place of application-specific circuits (ASICs); high-speed Ethernet and Infiniband are being used for interconnect in place of custom backplanes. Further, to lower hurdles in digital engineering, communities have designed and released general-purpose FPGA-based DSP systems, such as the CASPER ROACH board, ASTRON Uniboard, and CSIRO Redback board. In this introductory paper, we give a brief historical overview, a summary of recent trends, and provide an outlook on future directions.

scholarly journals Analysis of speech waveform quantization methods

2008 ◽  
Vol 18 (1) ◽  
pp. 19-22
Author(s):  
Predrag Tadic ◽  
Zeljko Djurovic ◽  
Branko Kovacevic
Keyword(s):  
Signal Processing ◽  
Probability Density Function ◽  
Digital Signal Processing ◽  
Speech Signal ◽  
Digital Signal ◽  
Quantization Noise ◽  
Signal Processing Algorithm ◽  
Memory Space ◽  
Pdf Estimation ◽  
Digital Signal Processing Algorithm

Digitalization, consisting of sampling and quantization, is the first step in any digital signal processing algorithm. In most cases, the quantization is uniform. However, having knowledge of certain stochastic attributes of the signal (namely, the probability density function, or pdf), quantization can be made more efficient, in the sense of achieving a greater signal to quantization noise ratio. This means that narrower channel bandwidths are required for transmitting a signal of the same quality. Alternatively, if signal storage is of interest, rather than transmission, considerable savings in memory space can be made. This paper presents several available methods for speech signal pdf estimation, and quantizer optimization in the sense of minimizing the quantization error power.

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