Vanderbilt University
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Guarding Against Universal Adversarial Perturbations in Data ...
by X Zhou · Cited by 1 — ciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1249, 2018. [45] BrainChip Incorporation, “The akida neural processor.
Framework Implementation and Experimental Setup
For the post-training quantization (PTQ) approach used by our framework, we use the BrainChip Akida CNN2SNN Toolkit [45], 2 which is a light-weight model quantization toolkit that is compatible with the Keras ML framework [46].
We consider three widely used computer vision datasets of CIFAR10/CIFAR100/SVHN. CIFAR10 and CIFAR100 datasets [47] are 32x32 colour images in 10/100 object classes, with 6000/600 images per class. It comprises 50,000 training images and 10,000 test images. SVHN [48] is a benchmark dataset containing over 600,000 labeled digits cropped from
2We thank Brainchip Inc for granting us academic use of codes and models from the Akida toolkit. Code:https://github.com
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