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WRA: A 2.2-to-6.3 TOPS Highly Unified Dynamically Reconfigurable Accelerator Using a Novel Winograd Decomposition Algorithm for Convolutional Neural Networks
PaperCitations, authors & open-access status
WRA: A 2.2-to-6.3 TOPS Highly Unified Dynamically Reconfigurable Accelerator Using a Novel Winograd Decomposition Algorithm for Convolutional Neural Networks is a paper indexed in ScholarIQ from OpenAlex. ScholarIQ records 45 citations, 2019 year and closed oa status.
45
Citations
2019
Year
closed
OA Status