1,686 research outputs found
Hardware Implementation of the GPS authentication
In this paper, we explore new area/throughput trade- offs for the Girault,
Poupard and Stern authentication protocol (GPS). This authentication protocol
was selected in the NESSIE competition and is even part of the standard ISO/IEC
9798. The originality of our work comes from the fact that we exploit a fixed
key to increase the throughput. It leads us to implement GPS using the Chapman
constant multiplier. This parallel implementation is 40 times faster but 10
times bigger than the reference serial one. We propose to serialize this
multiplier to reduce its area at the cost of lower throughput. Our hybrid
Chapman's multiplier is 8 times faster but only twice bigger than the
reference. Results presented here allow designers to adapt the performance of
GPS authentication to their hardware resources. The complete GPS prover side is
also integrated in the network stack of the PowWow sensor which contains an
Actel IGLOO AGL250 FPGA as a proof of concept.Comment: ReConFig - International Conference on ReConFigurable Computing and
FPGAs (2012
Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles
The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has
received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking
received support from the European Union’s Horizon 2020 research and innovation programme and Germany,
Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy,
Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL
Joint Undertaking under grant agreement No. 692455-2
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
The hardware implementation of deep neural networks (DNNs) has recently
received tremendous attention: many applications in fact require high-speed
operations that suit a hardware implementation. However, numerous elements and
complex interconnections are usually required, leading to a large area
occupation and copious power consumption. Stochastic computing has shown
promising results for low-power area-efficient hardware implementations, even
though existing stochastic algorithms require long streams that cause long
latencies. In this paper, we propose an integer form of stochastic computation
and introduce some elementary circuits. We then propose an efficient
implementation of a DNN based on integral stochastic computing. The proposed
architecture has been implemented on a Virtex7 FPGA, resulting in 45% and 62%
average reductions in area and latency compared to the best reported
architecture in literature. We also synthesize the circuits in a 65 nm CMOS
technology and we show that the proposed integral stochastic architecture
results in up to 21% reduction in energy consumption compared to the binary
radix implementation at the same misclassification rate. Due to fault-tolerant
nature of stochastic architectures, we also consider a quasi-synchronous
implementation which yields 33% reduction in energy consumption w.r.t. the
binary radix implementation without any compromise on performance.Comment: 11 pages, 12 figure
Minimalistic SDHC-SPI hardware reader module for boot loader applications
This paper introduces a low-footprint full hardware boot loading solution for FPGA-based Programmable
Systems on Chip. The proposed module allows loading the system code and data from a standard SD card
without having to re-program the whole embedded system. The hardware boot loader is processor independent
and removes the need of a software boot loader and the related memory resources. The hardware overhead
introduced is manageable, even in low-range FPGA chips, and negligible in mid- and high-range devices. The
implementation of the SD card reader module is explained in detail and an example of a multi-boot loader is
offered as well. The multi-boot loader is implemented and tested with the Xilinx's Picoblaze microcontroller
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