Trapezoid: A Versatile Accelerator for Dense and Sparse Matrix Multiplications

Accelerating matrix multiplication is crucial to achieve high performance in many application domains, including neural networks, graph analytics, and scientific computing. These applications process matrices with a wide range of sparsities, from completely dense to highly sparse. Ideally, a single...

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Bibliographic Details

Published in:	2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA) pp. 931 - 945
Main Authors:	Yang, Yifan, Emer, Joel S., Sanchez, Daniel
Format:	Conference Proceeding
Language:	English
Published:	IEEE 29.06.2024
Subjects:	Accelerator Computer architecture Costs Dataflow Hardware Matrix Multiplication Neural networks Scientific computing Sparse matrices Sparsity Throughput
Online Access:	Get full text
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