1,021 research outputs found

    Power Efficient Data-Aware SRAM Cell for SRAM-Based FPGA Architecture

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    The design of low-power SRAM cell becomes a necessity in today\u27s FPGAs, because SRAM is a critical component in FPGA design and consumes a large fraction of the total power. The present chapter provides an overview of various factors responsible for power consumption in FPGA and discusses the design techniques of low-power SRAM-based FPGA at system level, device level, and architecture levels. Finally, the chapter proposes a data-aware dynamic SRAM cell to control the power consumption in the cell. Stack effect has been adopted in the design to reduce the leakage current. The various peripheral circuits like address decoder circuit, write/read enable circuits, and sense amplifier have been modified to implement a power-efficient SRAM-based FPGA

    An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

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    Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

    Low power architectures for streaming applications

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    Supply Voltage Dependence of Heavy Ion Induced SEEs on 65nm CMOS Bulk SRAMs

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    The power consumption of Static Random Access Memory (SRAM) has become an important issue for modern integrated circuit design, considering the fact that they occupy large area and consume significant portion of power consumption in modern nanometer chips. SRAM operating in low power supply voltages has become an effective approach in reducing power consumption. Therefore, it is essential to experimentally characterize the single event effects (SEE) of hardened and unhardened SRAM cells to determine their appropriate applications, especially when a low supply voltage is preferred. In this thesis, a SRAM test chip was designed and fabricated with four cell arrays sharing the same peripheral circuits, including two types of unhardened cells (standard 6T and sub-threshold 10T) and two types of hardened cells (Quatro and DICE). The systems for functional and radiation tests were built up with power supply voltages that ranged from near threshold 0.4 V to normal supply 1 V. The test chip was irradiated with alpha particles and heavy ions with various linear energy transfers (LETs) at different core supply voltages, ranging from 1 V to 0.4 V. Experimental results of the alpha test and heavy ion test were consistent with the results of the simulation. The cross sections of 6T and 10T cells present much more significant sensitivities than Quatro and DICE cells for all tested supply voltages and LET. The 10T cell demonstrates a more optimal radiation performance than the 6T cell when LET is small (0.44 MeV·cm2/mg), yet no significant advantage is evident when LET is larger than this. In regards to the Quatro and DICE cells, one does not consistently show superior performance over the other in terms of soft error rates (SERs). Multi-bit upsets (MBUs) occupy a larger portion of total SEUs in DICE cell when relatively larger LET and smaller supply voltage are applied. It explains the loss in radiation tolerance competition with Quatro cell when LET is bigger than 9.1 MeV·cm2/mg and supply voltage is smaller than 0.6 V. In addition, the analysis of test results also demonstrated that the error amount distributions follow a Poisson distribution very well for each type of cell array

    A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

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    Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm2{}^\mathrm{2}. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

    Asynchronous Data Processing Platforms for Energy Efficiency, Performance, and Scalability

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    The global technology revolution is changing the integrated circuit industry from the one driven by performance to the one driven by energy, scalability and more-balanced design goals. Without clock-related issues, asynchronous circuits enable further design tradeoffs and in operation adaptive adjustments for energy efficiency. This dissertation work presents the design methodology of the asynchronous circuit using NULL Convention Logic (NCL) and multi-threshold CMOS techniques for energy efficiency and throughput optimization in digital signal processing circuits. Parallel homogeneous and heterogeneous platforms implementing adaptive dynamic voltage scaling (DVS) based on the observation of system fullness and workload prediction are developed for balanced control of the performance and energy efficiency. Datapath control logic with NULL Cycle Reduction (NCR) and arbitration network are incorporated in the heterogeneous platform for large scale cascading. The platforms have been integrated with the data processing units using the IBM 130 nm 8RF process and fabricated using the MITLL 90 nm FDSOI process. Simulation and physical testing results show the energy efficiency advantage of asynchronous designs and the effective of the adaptive DVS mechanism in balancing the energy and performance in both platforms

    Low power techniques for video compression

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    This paper gives an overview of low-power techniques proposed in the literature for mobile multimedia and Internet applications. Exploitable aspects are discussed in the behavior of different video compression tools. These power-efficient solutions are then classified by synthesis domain and level of abstraction. As this paper is meant to be a starting point for further research in the area, a lowpower hardware & software co-design methodology is outlined in the end as a possible scenario for video-codec-on-a-chip implementations on future mobile multimedia platforms

    Low-power digital processor for wireless sensor networks

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 69-72).In order to make sensor networks cost-effective and practical, the electronic components of a wireless sensor node need to run for months to years on the same battery. This thesis explores the design of a low-power digital processor for these sensor nodes, employing techniques such as hardwired algorithms, lowered supply voltages, clock gating and subsystem shutdown. Prototypes were built on both a FPGA and ASIC platform, in order to verify functionality and characterize power consumption. The resulting 0.18[micro]m silicon fabricated in National Semiconductor Corporation's process was operational for supply voltages ranging from 0.5V to 1.8V. At the lowest operating voltage of 0.5V and a frequency of 100KHz, the chip performs 8 full-accuracy FFT computations per second and draws 1.2nJ of total energy per cycle. Although this energy/cycle metric does not surpass existing low-energy processors demonstrated in literature or commercial products, several low-power techniques are suggested that could drastically improve the energy metrics of a future implementation.by Daniel Frederic Finchelstein.S.M
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