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TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing

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Published in:ArXiv cs.ET Recent Papers
Format: Online Article RSS Article
Published: 2026
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spellingShingle TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing
ArXiv cs.ET Recent Papers
Electrical & Electronics
Engineering & Technology
sub_discipline_display Electrical & Electronics
sub_discipline_facet Electrical & Electronics
subject_display ArXiv cs.ET Recent Papers
Electrical & Electronics
Engineering & Technology
ArXiv cs.ET Recent Papers
Electrical & Electronics
Engineering & Technology
subject_facet ArXiv cs.ET Recent Papers
Electrical & Electronics
Engineering & Technology
title TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing
title_auth TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing
title_full TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing
title_fullStr TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing
title_full_unstemmed TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing
title_short TetrisG-SDK: Efficient Convolutional Layer Mapping with Adaptive Windows and Grouped Convolutions for Fast In-Memory Computing
title_sort tetrisg-sdk: efficient convolutional layer mapping with adaptive windows and grouped convolutions for fast in-memory computing
topic ArXiv cs.ET Recent Papers
Electrical & Electronics
Engineering & Technology
url https://arxiv.org/abs/2604.25377v1