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The Challenge of Multi-Operand Adders in CNNs on FPGAs: How Not to Solve It!

Abstract : Convolutional Neural Networks (CNNs) are computationally intensive algorithms that currently require dedicated hardware to be executed. In the case of FPGA-Based accelerators, we point-out in this work the challenge of Multi-Operand Adders (MOAs) and their high resource utilization in an FPGA implementation of a CNN. To address this challenge, two optimization strategies, that rely on time-multiplexing and approximate computing, are investigated. At first glance, the two strategies looked promising to reduce the footprint of a given architectural mapping, but when synthesized on the device, none of them gave the expected results. Experimental sections analyze the reasons of these unexpected results.
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Submitted on : Friday, January 18, 2019 - 11:04:46 AM
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Kamel Abdelouahab, Maxime Pelcat, François Berry. The Challenge of Multi-Operand Adders in CNNs on FPGAs: How Not to Solve It!. 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS '18), Jul 2018, Pythagorion, Greece. pp.157-160, ⟨10.1145/3229631.3235024⟩. ⟨hal-01902952⟩

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