Application-Specific Accelerators for Communications

Background: With the growing demand of image processing and the use of Digital Signal Processors (DSP), the efficiency of the Multipliers and Accumulators has become a bottleneck to get through. We revised a few patents on an Application Specific Instruction Set Processor (ASIP), where the design considerations are proposed for application-specific computing in an efficient way to enhance the throughput. Objective: The study aims to develop and analyze a computationally efficient method to optimize the speed performance of MAC. Methods: The work presented here proposes the design of an Application Specific Instruction Set Processor, exploiting a Multiplier Accumulator integrated as the dedicated hardware. This MAC is optimized for high-speed performance and is the application-specific part of the processor; here it can be the DSP block of an image processor while a 16-bit Reduced Instruction Set Computer (RISC) processor core gives the flexibility to the design for any computing. The design was emulated on a Xilinx Field Programmable Gate Array (FPGA) and tested for various real-time computing. Results: The synthesis of the hardware logic on FPGA tools gave the operating frequencies of the legacy methods and the proposed method, the simulation of the logic verified the functionality. Conclusion: With the proposed method, a significant improvement of 16% increase in throughput has been observed for 256 steps iterations of multiplier and accumulators on an 8-bit sample data. Such an improvement can help in reducing the computation time in many digital signal processing applications where multiplication and addition are done iteratively.

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Designing a Reliable and Application Specific Controller Area Network Protocol for Intra-Communication of an Embedded System

Research Journal of Applied Sciences Engineering and Technology ◽

10.19026/rjaset.5.4971 ◽

2013 ◽

Vol 5 (2) ◽

pp. 445-451

Author(s):

Seyyed Amir Asghari ◽

Hassan Taheri ◽

Hossein Pedram

Keyword(s):

Embedded System ◽

Controller Area Network ◽

Network Protocol ◽

Area Network ◽

Application Specific

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An FPGA-based Application-Specific Processor for Implementing the Exponential Function

2020 SoutheastCon ◽

10.1109/southeastcon44009.2020.9249746 ◽

2020 ◽

Author(s):

Shrikant S. Jadhav ◽

Clay Gloster ◽

Jannatun Naher ◽

Christopher Doss ◽

Youngsoo Kim

Keyword(s):

Exponential Function ◽

Application Specific

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EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

Applied Spectroscopy ◽

10.1177/0003702820987847 ◽

2021 ◽

pp. 000370282098784

Author(s):

James Renwick Beattie ◽

Francis Esmonde-White

Keyword(s):

Principal Components ◽

Principal Component ◽

Original Data ◽

Specific Chemical ◽

Successive Refinement ◽

Minimal Loss ◽

Components Analysis ◽

The Mathematical Model ◽

Application Specific ◽

Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing âconcentrationâ or âweightsâ. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

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