INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing

An increasing number of researchers are finding use for nth-order gradient computations for a wide variety of applications, including graphics, meta-learning (MAML), scientific computing, and most recently, implicit neural representations (INRs). Recent work shows that the gradient of an INR can be...

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Veröffentlicht in:	Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design S. 1 - 9
Hauptverfasser:	Abi-Karam, Stefan, Sarkar, Rishov, Xu, Dejia, Fan, Zhiwen, Wang, Zhangyang, Hao, Cong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 28.10.2023
Schlagworte:	Adaptation models Computational efficiency Computational modeling Computer architecture Graphics processing units Throughput Transforms
ISSN:	1558-2434
Online-Zugang:	Volltext
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