2012Unpublished venueRequires access

Implementing Time-Derivative CNNs on a Xilinx Spartan FPGA

Jordi Albó-Canals, Giovanni E. Pazienza

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Abstract

Time-Derivative CNNs (TDCNNs) have been recently proposed as a novel paradigm realizing spatiotemporal transfer functions for linear filtering. Their dynamics is usually simulated with SIMULINK because VLSI chips are still in the preliminary phase. In order to make TDCNNs available to a larger audience, we present here their implementation on a Xilinx Spartan-6 FPGA. The results concerning an 8×8 network are promising and consistent with the SW simulations.

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What this paper is about

Time-Derivative CNNs (TDCNNs) have been recently proposed as a novel paradigm realizing spatiotemporal transfer functions for linear filtering. Their dynamics is usually simulated with SIMULINK because VLSI chips are still in the preliminary phase. In order to make TDCNNs available to a larger audience, we present here their implementation on a Xilinx Spartan-6 FPGA. The results concerning an 8×8 network are promising and consistent with the SW simulations.

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Available abstract

Time-Derivative CNNs (TDCNNs) have been recently proposed as a novel paradigm realizing spatiotemporal transfer functions for linear filtering. Their dynamics is usually simulated with SIMULINK because VLSI chips are still in the preliminary phase. In order to make TDCNNs available to a larger audience, we present here their implementation on a Xilinx Spartan-6 FPGA. The results concerning an 8×8 network are promising and consistent with the SW simulations.

Key concepts: Spartan, Field-programmable gate array, Computer science, Very-large-scale integration, Embedded system, Derivative (finance), Computer architecture, Computer hardware

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