What is it about?

Low-cost arithmetic is fundamental for embedded deep learning. We provide very efficient arithmetic units for the design of deep neural networks in embedded FPGAs

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Why is it important?

Hardware architectures for deep learning computing have thousands of cores. Each of these cores has basic parallel multiply-accumulation units. The efficient design of these units determines the hardware solution's final cost and the throughput performance. We were able to design very efficient fused multiply-add units that improve the area and performance of dot-product units.


This work opens new perspectives on applying FPGAs in the design of deep learning models.

Mário Véstias

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This page is a summary of: Efficient Design of Low Bitwidth Convolutional Neural Networks on FPGA with Optimized Dot Product Units, ACM Transactions on Reconfigurable Technology and Systems, July 2022, ACM (Association for Computing Machinery), DOI: 10.1145/3546182.
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