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arXiv · 1807.00217

The Challenge of Multi-Operand Adders in CNNs on FPGAs: How not to solve it!

Kamel Abdelouahab·François Berry·Maxime Pelcat
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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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Kamel Abdelouahab, François Berry, Maxime Pelcat. 2018-06-30. The Challenge of Multi-Operand Adders in CNNs on FPGAs: How not to solve it!. https://arxiv.org/abs/1807.00217

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