XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing

Abhishek Moitra, Abhiroop Bhattacharjee, Youngeun Kim, Priyadarshini Panda
3/30/2023
cs.CV

No official code found. Best implementation: Intelligent-Computing-Lab-Panda/XPert (5 stars, last commit 2024-01-06).

Abstract

The hardware-efficiency and accuracy of Deep Neural Networks (DNNs) implemented on In-memory Computing (IMC) architectures primarily depend on the DNN architecture and the peripheral circuit parameters. It is therefore essential to holistically co-search the network and peripheral parameters to achieve optimal performance. To this end, we propose XPert, which co-searches network architecture in tandem with peripheral parameters such as the type and precision of analog-to-digital converters, crossbar column sharing and the layer-specific input precision using an optimization-based design space exploration. Compared to VGG16 baselines, XPert achieves 10.24x (4.7x) lower EDAP, 1.72x (1.62x) higher TOPS/W,1.93x (3x) higher TOPS/mm2 at 92.46% (56.7%) accuracy for CIFAR10 (TinyImagenet) datasets. The code for this paper is available at https://github.com/Intelligent-Computing-Lab-Yale/XPert.

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Code Implementations(1)

Intelligent-Computing-Lab-Panda/XPertCommunity100%
53C, C++Mar 30, 20232 years ago

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Cite this paper

@article{moitra2023xpert,
  title  = {XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing},
  author = {Abhishek Moitra and Abhiroop Bhattacharjee and Youngeun Kim and Priyadarshini Panda},
  year   = {2023},
  eprint = {2303.17646},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.17646v2}
}

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