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Purpose In this work, an efficient architecture for memory built in self-test (MBIST) that incorporates a modified March Y algorithm using concurrent technique and a modified linear feedback shift register (LFSR)–based address generator is proposed. Design/methodology/approach Built in self-test (BIST) is emerging as the essential ingredient of the system on chip. In the ongoing high speed, high tech sophistication technology of the very large-scale integrated circuits, testing of these memories is a very tedious and challenging job, since the area overhead, the testing time and the cost of the test play an important role. Findings With the efficient service of the adapted architecture, switching activity is considerably cut down. As the switching activity is in direct proportion to the power consumed scaling down, the switching process of the address generator inevitably leads to the reduction in power consumption of the MBIST. Originality/value To improve the yield and fault tolerance of on-chip memories without degradation on its performance self-repair mechanisms can be implemented on chip.

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Compressive Sensing is an innovative platform for signal processing, which offers more practical methods to solve the issues of voluminous useless data genera ted during the series of processes associated with conventional signal processing paradigm, which are based on the traditional sampling theorem. The Compressive Sensing theory proposes that sparse signals can be successfully reconstructed from very few sampies which are acquired at a much lower rate than the Nyquist rate. The theory is trying to combine the process of sampling, encoding and compressing into a simple and single step process. This concept has the potential to surpass the limits of Sampling Theorem and can perform better to deal with the related problems. The compressive sensing scenario is portrayed with this paper in an image processing environment. Aside from that, the paper puts an effort to analyze the processing and storage challenges associated with the conventional standards. The paper also proposes a prospective approach to resolve the issues concerned by using Compressive Sensing as an effective instrument. From this study and analysis it can be concluded that many of the challenges in the conventional methods can be defied contentedly with the help of Compressive Sensing.