1,199 research outputs found

    LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

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    LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft

    High-Level Synthesis for Embedded Systems

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    NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

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    © 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation

    FPGA Implementation of Low-Area Square Root Calculator

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    Square root is one of the mathematical operations which are widely used in digital signal processing. Its implementation on hardware such as FPGA will provide several advantages compare to the performance offered in software. There are several algorithms which can be utilized for this calculation, but they are difficult to be implemented in FPGA. This paper presents a model of FPGA based square root calculator, which requires very low resources usage, thus occupying very low area of FPGA. The model is designed to suit the needs of medium-speed and low-speed applications which don’t need very high processing speed, while optimizing the number of resources utilized.The modified non-restoring algorithm is used in this design to compute the square root. The design is coded in RTL VHDL, and implemented in Altera DE2-board for hardware validation. The implementation produced very precise square root calculation, with low latency computation and low area consumption, for various input data width tested
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