Embedded Memories for Nano-Scale VLSIs (Integrated Circuits by Kevin Zhang

By Kevin Zhang

The booklet presents a finished and in-depth view at the cutting-edge embedded reminiscence applied sciences. The ebook is helping working towards engineers grab key expertise attributes and complex layout ideas in nano-scale VLSI layout. It additionally is helping them make judgements about the correct layout tradeoffs in actual product improvement. This ebook first offers an summary at the panorama and development of embedded reminiscence in a number of VLSI method designs, together with high-performance microprocessor, low-power cellular hand held units, micro-controllers, and numerous client electronics. It then indicates an in-depth view on every one diverse form of embedded reminiscence know-how, together with high-speed SRAM, ultra-low-voltage and replacement SRAM, embedded DRAM, embedded nonvolatile reminiscence, and rising or so-called “universal” stories equivalent to FeRAM, MRAM, and PRAM. each one subject covers the entire key know-how attributes from a product program standpoint, starting from expertise scaling demanding situations to complex circuit thoughts for reaching optimum layout tradeoff in functionality and tool. As VLSI structures develop into more and more depending on on-die reminiscence to supply enough reminiscence bandwidth for varied functions, the e-book offers readers a broader view of this crucial box and is helping them to accomplish their optimum layout objectives for various functions. This publication offers readers a huge wisdom at the complete embedded reminiscence applied sciences that allows you to higher understand the applied sciences and create optimum reminiscence recommendations in actual functions.

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Extra info for Embedded Memories for Nano-Scale VLSIs (Integrated Circuits and Systems)

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And the interconnection inside the die does not need large buffers like pad drivers. Another benefit is that the embedded memory does not need to follow conventional memory interface which is standard but somewhat redundant. Details will be shown through the 3D graphics rendering engine design with embedded DRAM memory. 19 shows the architecture of 3D graphics rendering processor [11]. Totally 29 Mb DRAMs are split into three memory modules: frame buffer, depth Fig. 18 Large bandwidth approaches for system-on-board design 2 Embedded Memory Architecture for Low-Power Application Processor 31 Fig.

David G. Lowe, “Distinctive Image Features from Scale-Invariant Key points,” ACM Intl. Journal of Computer Vision, Vol. 60, Issue 2, pp. 91–110, 2004. 19. , “Data Association Using Visual Object Recognition for EKF-SLAM in Home Environment,” Proceedings of IEEE Intl. Conf. on Intelligent Robots and Systems, pp. 2760–2765, 2006. 20. , “Augmenting SLAM with Object Detection in a Service Robot Framework,” IEEE Intl. Symposium on Robot and Human Interactive Communication, pp. 741–746, 2006. 21. , ”SLAM using Visual Scan-Matching with Distinguishable 3D Points,” IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.

The increasing VT random variation degrades SRAM functional margins, resulting in an increase of the minimum SRAM VDD [28–30]. 3 Fig. 2 Functional Margin Issues with Scaled Nanometer-Scale SRAM SRAM functional margins are determined by three key SRAM design parameters of static noise margin (SNM), write margin (WRM), and cell current (Icell). Since all of them strongly depend on operating voltage (VDD ), transistor channel length (Lg), and width (Wg), scaling of all these key parameters will be examined in details in this section.

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