Semi-Supervised Deep Learning for Microcontroller Performance Screening

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of F_max (the maximum operating frequency). Data extracted from on-chip ring oscillators (ROs) can model the F_max of integrated circuits using machine learning models. Those models are suitable for the performance screening process. Acquiring data from the ROs is a fast process that leads to many unlabeled data. Contrarily, the labeling phase (i.e., acquiring F_max) is a time-consuming and costly task, that leads to a small set of labeled data. This paper presents deep-learning-based methodologies to cope with the low number of labeled data in microcontroller performance screening. We propose a method that takes advantage of the high number of unlabeled samples in a semi-supervised learning fashion. We derive deep feature extractor models that project data into higher dimensional spaces and use the data feature embedding to face the performance prediction problem with simple linear regression. Experiments showed that the proposed models outperformed state-of-the-art methodologies in terms of prediction error and permitted us to use a significantly smaller number of devices to be characterized, thus reducing the time needed to build ML models by a factor of six with respect to baseline approaches

Improving Resnet-9 Generalization Trained on Small Datasets

This paper presents our proposed approach that won the first prize at the ICLR competition on Hardware Aware Efficient Training. The challenge is to achieve the highest possible accuracy in an image classification task in less than 10 minutes. The training is done on a small dataset of 5000 images picked randomly from CIFAR-10 dataset. The evaluation is performed by the competition organizers on a secret dataset with 1000 images of the same size. Our approach includes applying a series of technique for improving the generalization of ResNet-9 including: sharpness aware optimization, label smoothing, gradient centralization, input patch whitening as well as metalearning based training. Our experiments show that the ResNet-9 can achieve the accuracy of 88% while trained only on a 10% subset of CIFAR-10 dataset in less than 10 minuet

Sinc-based convolutional neural networks for EEG-BCI-based motor imagery classification

Brain-Computer Interfaces (BCI) based on motor imagery translate mental motor images recognized from the electroencephalogram (EEG) to control commands. EEG patterns of different imagination tasks, e.g. hand and foot movements, are effectively classified with machine learning techniques using band power features. Recently, also Convolutional Neural Networks (CNNs) that learn both effective features and classifiers simultaneously from raw EEG data have been applied. However, CNNs have two major drawbacks: (i) they have a very large number of parameters, which thus requires a very large number of training examples; and (ii) they are not designed to explicitly learn features in the frequency domain. To overcome these limitations, in this work we introduce Sinc-EEGNet, a lightweight CNN architecture that combines learnable band-pass and depthwise convolutional filters. Experimental results obtained on the publicly available BCI Competition IV Dataset 2a show that our approach outperforms reference methods in terms of classification accuracy