214 research outputs found

    Cost and Coding Efficient Motion Estimation Design Considerations for High Efficiency Video Coding (HEVC) Standard

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    This paper focuses on motion estimation engine design in future high-efficiency video coding (HEVC) encoders. First, a methodology is explained to analyze hardware implementation cost in terms of hardware area, memory size and memory bandwidth for various possible motion estimation engine designs. For 11 different configurations, hardware cost as well as the coding efficiency are quantified and are compared through a graphical analysis to make design decisions. It has been shown that using smaller block sizes (e.g. 4 × 4) imposes significantly larger hardware requirements at the expense of modest improvements in coding efficiency. Secondly, based on the analysis on various configurations, one configuration is chosen and algorithm improvements are presented to further reduce hardware implementation cost of the selected configuration. Overall, the proposed changes provide 56 × on-chip bandwidth, 151 × off-chip bandwidth, 4.3 × core area and 4.5 × on-chip memory area savings when compared to the hardware implementation of the HM-3.0 design.Texas Instruments Incorporate

    A 1.1 nW Energy-Harvesting System with 544 pW Quiescent Power for Next-Generation Implants

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    This paper presents a nW power management unit (PMU) for an autonomous wireless sensor that sustains itself by harvesting energy from the endocochlear potential (EP), the 70-100 mV electrochemical bio-potential inside the mammalian ear. Due to the anatomical constraints inside the inner ear, the total extractable power from the EP is limited close to 1.1-6.25 nW. A nW boost converter is used to increase the input voltage (30-55 mV) to a higher voltage (0.8-1.1 V) usable by CMOS circuits in the sensor. A pW charge pump circuit is used to minimize the leakage in the boost converter. Furthermore, ultralow-power control circuits consisting of digital implementations of input impedance adjustment circuits and zero current switching circuits along with Timer and Reference circuits keep the quiescent power of the PMU down to 544 pW. The designed boost converter achieves a peak power conversion efficiency of 56%. The PMU can sustain itself, and a duty-cyled ultralow-power load while extracting power from the EP of a live guinea pig. The PMU circuits have been implemented on a 0.18- μm CMOS process.Semiconductor Research Corporation. Focus Center for Circuit and System Solutions (C2S2)Interconnect Focus Center (United States. Defense Advanced Research Projects Agency and Semiconductor Research Corporation)National Institutes of Health (U.S.) (Grant K08 DC010419)National Institutes of Health (U.S.) (Grant T32 DC00038)Bertarelli Foundatio

    A Sub-nW 2.4 GHz Transmitter for Low Data-Rate Sensing Applications

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    This paper presents the design of a narrowband transmitter and antenna system that achieves an average power consumption of 78 pW when operating at a duty-cycled data rate of 1 bps. Fabricated in a 0.18 μm CMOS process, the transmitter employs a direct-RF power oscillator topology where a loop antenna acts as a both a radiative and resonant element. The low-complexity single-stage architecture, in combination with aggressive power gating techniques and sizing optimizations, limited the standby power of the transmitter to only 39.7 pW at 0.8 V. Supporting both OOK and FSK modulations at 2.4 GHz, the transmitter consumed as low as 38 pJ/bit at an active-mode data rate of 5 Mbps. The loop antenna and integrated diodes were also used as part of a wireless power transfer receiver in order to kick-start the system power supply prior to energy harvesting operation.Semiconductor Research Corporation. Interconnect Focus CenterSemiconductor Research Corporation. C2S2 Focus CenterNational Institutes of Health (U.S.) (Grant K08 DC010419)National Institutes of Health (U.S.) (Grant T32 DC00038)Bertarelli Foundatio

    Energy-efficient waveform for electrical stimulation of the cochlear nerve

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    The cochlear implant (CI) is the most successful neural prosthesis, restoring the sensation of sound in people with severe-to-profound hearing loss by electrically stimulating the cochlear nerve. Existing CIs have an external, visible unit, and an internal, surgically-placed unit. There are significant challenges associated with the external unit, as it has limited utility and CI users often report a social stigma associated with prosthesis visibility. A fully-implantable CI (FICI) would address these issues. However, the volume constraint imposed on the FICI requires less power consumption compared to today’s CI. Because neural stimulation by CI electrodes accounts for up to 90% of power consumption, reduction in stimulation power will result directly in CI power savings. To determine an energy-efficient waveform for cochlear nerve stimulation, we used a genetic algorithm approach, incorporating a computational model of a single mammalian myelinated cochlear nerve fiber coupled to a stimulator-electrode-tissue interface. The algorithm’s prediction was tested in vivo in human CI subjects. We find that implementation of a non-rectangular biphasic neural stimulation waveform may result in up to 25% charge savings and energy savings within the comfortable range of hearing for CI users. The alternative waveform may enable future development of a FICI

    Robust SAT-Based Search Algorithm for Leakage Power Reduction

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    Self-aware Computing in the Angstrom Processor

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    Addressing the challenges of extreme scale computing requires holistic design of new programming models and systems that support those models. This paper discusses the Angstrom processor, which is designed to support a new Self-aware Computing (SEEC) model. In SEEC, applications explicitly state goals, while other systems components provide actions that the SEEC runtime system can use to meet those goals. Angstrom supports this model by exposing sensors and adaptations that traditionally would be managed independently by hardware. This exposure allows SEEC to coordinate hardware actions with actions specified by other parts of the system, and allows the SEEC runtime system to meet application goals while reducing costs (e.g., power consumption).United States. Defense Advanced Research Projects Agency. The Ubiquitous High Performance Computing Progra

    Testability Analysis of Circuits using Data-Dependent Power Management

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    Energy extraction from the biologic battery in the inner ear

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    Endocochlear potential (EP) is a battery-like electrochemical gradient found in and actively maintained by the inner ear [superscript 1, 2]. Here we demonstrate that the mammalian EP can be used as a power source for electronic devices. We achieved this by designing an anatomically sized, ultra-low quiescent-power energy harvester chip integrated with a wireless sensor capable of monitoring the EP itself. Although other forms of in vivo energy harvesting have been described in lower organisms [superscript 3, 4, 5], and thermoelectric [superscript 6], piezoelectric [superscript 7] and biofuel [superscript 8, 9] devices are promising for mammalian applications, there have been few, if any, in vivo demonstrations in the vicinity of the ear, eye and brain. In this work, the chip extracted a minimum of 1.12 nW from the EP of a guinea pig for up to 5 h, enabling a 2.4 GHz radio to transmit measurement of the EP every 40–360 s. With future optimization of electrode design, we envision using the biologic battery in the inner ear to power chemical and molecular sensors, or drug-delivery actuators for diagnosis and therapy of hearing loss and other disorders.Focus Center Research Program. Focus Center for Circuit & System Solutions. Semiconductor Research Corporation. Interconnect Focus CenterNational Institutes of Health (U.S.) (Grant K08 DC010419)National Institutes of Health (U.S.) (Grant T32 DC00038)Bertarelli Foundatio

    Digital Signal Processing Research Program

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    Contains table of contents for Section 2, an introduction, reports on twenty research projects and a list of publications.Lockheed Sanders, Inc. Contract BZ4962U.S. Army Research Laboratory Grant QK-8819U.S. Navy - Office of Naval Research Grant N00014-93-1-0686National Science Foundation Grant MIP 95-02885U.S. Navy - Office of Naval Research Grant N00014-95-1-0834U.S. Navy - Office of Naval Research Grant N00014-96-1-0930U.S. Navy - Office of Naval Research Grant N00014-95-1-0362National Defense Science and Engineering FellowshipU.S. Air Force - Office of Scientific Research Grant F49620-96-1-0072National Science Foundation Graduate Research Fellowship Grant MIP 95-02885Lockheed Sanders, Inc. Grant N00014-93-1-0686National Science Foundation Graduate FellowshipU.S. Army Research Laboratory/ARL Advanced Sensors Federated Lab Program Contract DAAL01-96-2-000
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