No abstract

Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the setting in which training data was collected. Furthermore, manual labeling of EEG signals is a time-consuming process requiring expert analysis, making fine-tuning patient-specific models to new settings a costly proposition. In this work, we propose the Maximum-Mean-Discrepancy Decoder (M2D2) for automatic temporal localization and labeling of seizures in long EEG recordings to assist medical experts. We show that M2D2 achieves 76.0% and 70.4% of F1-score for temporal localization when evaluated on EEG data gathered in a different clinical setting than the training data. The results demonstrate that M2D2 yields substantially higher generalization performance than other state-of-the-art deep learning-based approaches.

Transformer models have achieved impressive results in various AI scenarios, ranging from vision to natural language processing. However, their computational complexity and their vast number of parameters hinder their implementations on resource-constrained platforms. Furthermore, while loosely-coupled hardware accelerators have been proposed in the literature, data transfer costs limit their speed-up potential. We address this challenge along two axes. First, we introduce tightly-coupled, small-scale systolic arrays (TiC-SATs), governed by dedicated ISA extensions, as dedicated functional units to speed up execution. Then, thanks to the tightly-coupled architecture, we employ software optimizations to maximize data reuse, thus lowering miss rates across cache hierarchies. Full system simulations across various BERT and Vision-Transformer models are employed to validate our strategy, resulting in substantial application-wide speed-ups (e.g., up to 89.5X for BERT-large). TiC-SAT is available as an open-source framework 1 . CCS CONCEPTS• Computer systems organization → Neural networks; Systolic arrays; • Computing methodologies → Natural language processing; • Hardware → Hardware-software codesign.