IP3_2 Interactive Presentations
Date: Tuesday, 02 February 2021
Time: 18:30 - 19:00 CET
Virtual Conference Room: https://virtual21.date-conference.com/meetings/virtual/G2ovS3MSZWxuNM6cP
Interactive Presentations run simultaneously during a 30-minute slot. Additionally, each IP paper is briefly introduced in a one-minute presentation in a corresponding regular session
|IP3_2.1||GENERIC SAMPLE PREPARATION FOR DIFFERENT MICROFLUIDIC PLATFORMS
Sudip Poddar, Johannes Kepler University Linz, Austria, AT
Sudip Poddar1, Gerold Fink2, Werner Haselmayr1 and Robert Wille2
1Johannes Kepler University, AT; 2Johannes Kepler University Linz, AT
Sample preparation plays a crucial role in several medical applications. Microfluidic devices or Labs-on-Chips (LoCs) got established as a suitable solution to realize this task in a miniaturized, integrated, and automatic fashion. Over the years, a variety of different microfluidic platforms emerged, which all have their respective pros and cons. Accordingly, numerous approaches for sample preparation have been proposed—each specialized on a single platform only. In this work, we propose an idea towards a generic sample preparation approach which will generalize the constraints of the different microfluidic platforms and, by this, will provide a platform-independent sample preparation method. This will allow designers to quickly check what existing platform is most suitable for the considered task and to easily support upcoming and future microfluidic platforms as well. We illustrate the applicability of the proposed method with examples for various platforms.
|IP3_2.2||RAISE: A RESISTIVE ACCELERATOR FOR SUBJECT-INDEPENDENT EEG SIGNAL CLASSIFICATION
Fan Chen, Duke University, US
Fan Chen1, Linghao Song1, Hai (Helen) Li2 and Yiran Chen1
1Duke University, US; 2Duke University/TUM-IAS, US
State-of-the-art deep neural networks (DNNs) for electroencephalography (EEG) signals classification focus on subject-related tasks, in which the test data and the training data needs to be collected from the same subject. In addition, due to limited computing resources and strict power budgets at edges, it is very challenging to deploy the inference of such DNN models on biological devices. In this work, we present an algorithm/hardware co-designed low-power accelerator for subject-independent EEG signal classification. We propose a compact neural network that is capable to identify the common and stable structure among subjects. Based on it, we realize a robust subject-independent EEG signal classification model that can be extended to multiple BCI tasks with minimal overhead. Based on this model, we present RAISE, a low-power processing-in-memory inference accelerator by leveraging the emerging resistive memory. We compare the proposed model and hardware accelerator to prior arts across various BCI paradigms. We show that our model achieves the best subject-independent classification accuracy, while RAISE achieves 2.8x power reduction and 2.5x improvement in performance per watt compared to the state-of-the-art resistive inference accelerator.