Accelerating CNN inference on FPGAs: A Survey

Dev.to / 5/3/2026

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Key Points

The article is a survey focused on methods for accelerating Convolutional Neural Network (CNN) inference specifically on FPGA hardware.
It covers architectural and implementation approaches aimed at improving throughput and/or reducing latency during CNN forward-pass execution on reconfigurable logic.
The survey emphasizes practical design choices that affect performance, such as mapping strategies, dataflow, and hardware utilization for FPGA-based inference.
It consolidates prior work and trends to help readers compare different acceleration techniques for FPGA deployment of CNN models.

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