NN2FPGA: Optimizing CNN Inference on FPGAs With Binary Integer Programming

Skip connections have emerged as a key component of modern convolutional neural networks (CNNs) for computer vision tasks, allowing for the creation of more accurate and deeper models by addressing the vanishing gradient problem. However, the existing implementations of field-programmable gate array...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems Jg. 44; H. 5; S. 1807 - 1818
Hauptverfasser:	Bosio, Roberto, Minnella, Filippo, Urso, Teodoro, Casu, Mario R., Lavagno, Luciano, Lazarescu, Mihai T., Pasini, Paolo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accelerators Accuracy Artificial neural networks Codesign Compilers Computational modeling Computer vision Convolutional codes Convolutional neural networks dataflow architecture Design of experiments Design optimization Field programmable gate arrays field-programmable gate array (FPGA) acceleration graph optimization High level synthesis Integer programming Machine learning Network latency Optimization pipelining quantization Quantization (signal) residual neural networks (NNs) Resource utilization System-on-chip Throughput
ISSN:	0278-0070, 1937-4151
Online-Zugang:	Volltext
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