969 research outputs found

    Mapping the SISO module of the Turbo decoder to a FPFA

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    In the CHAMELEON project a reconfigurable systems-architecture, the Field Programmable Function Array (FPFA) is introduced. FPFAs are reminiscent to FPGAs, but have a matrix of ALUs and lookup tables instead of Configurable Logic Blocks (CLBs). The FPFA can be regarded as a low power reconfigurable accelerator for an application specific domain. In this paper we show how the SISO (Soft Input Soft Output) module of the Turbo decoding algorithm can be mapped on the reconfigurable FPFA

    ADEPOS: Anomaly Detection based Power Saving for Predictive Maintenance using Edge Computing

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    In industry 4.0, predictive maintenance(PM) is one of the most important applications pertaining to the Internet of Things(IoT). Machine learning is used to predict the possible failure of a machine before the actual event occurs. However, the main challenges in PM are (a) lack of enough data from failing machines, and (b) paucity of power and bandwidth to transmit sensor data to cloud throughout the lifetime of the machine. Alternatively, edge computing approaches reduce data transmission and consume low energy. In this paper, we propose Anomaly Detection based Power Saving(ADEPOS) scheme using approximate computing through the lifetime of the machine. In the beginning of the machines life, low accuracy computations are used when the machine is healthy. However, on the detection of anomalies, as time progresses, the system is switched to higher accuracy modes. We show using the NASA bearing dataset that using ADEPOS, we need 8.8X less neurons on average and based on post-layout results, the resultant energy savings are 6.4 to 6.65XComment: Submitted to ASP-DAC 2019, Japa

    Optimization of DSSS Receivers Using Hardware-in-the-Loop Simulations

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    Over the years, there has been significant interest in defining a hardware abstraction layer to facilitate code reuse in software defined radio (SDR) applications. Designers are looking for a way to enable application software to specify a waveform, configure the platform, and control digital signal processing (DSP) functions in a hardware platform in a way that insulates it from the details of realization. This thesis presents a tool-based methodolgy for developing and optimizing a Direct Sequence Spread Spectrum (DSSS) transceiver deployed in custom hardware like Field Programmble Gate Arrays (FPGAs). The system model consists of a tranmitter which employs a quadrature phase shift keying (QPSK) modulation scheme, an additive white Gaussian noise (AWGN) channel, and a receiver whose main parts consist of an analog-to-digital converter (ADC), digital down converter (DDC), image rejection low-pass filter (LPF), carrier phase locked loop (PLL), tracking locked loop, down-sampler, spread spectrum correlators, and rectangular-to-polar converter. The design methodology is based on a new programming model for FPGAs developed in the industry by Xilinx Inc. The Xilinx System Generator for DSP software tool provides design portability and streamlines system development by enabling engineers to create and validate a system model in Xilinx FPGAs. By providing hierarchical modeling and automatic HDL code generation for programmable devices, designs can be easily verified through hardware-in-the-loop (HIL) simulations. HIL provides a significant increase in simulation speed which allows optimization of the receiver design with respect to the datapath size for different functional parts of the receiver. The parameterized datapath points used in the simulation are ADC resolution, DDC datapath size, LPF datapath size, correlator height, correlator datapath size, and rectangular-to-polar datapath size. These parameters are changed in the software enviornment and tested for bit error rate (BER) performance through real-time hardware simualtions. The final result presents a system design with minimum harware area occupancy relative to an acceptable BER degradation
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