Teacher: Prof. Keshab Parhi

Lesson Synopsis
This lesson explores machine learning applications in data-driven neuroscience, and low-energy implementations of machine learning systems. Data-driven neuroscience can exploit machine learning approaches including deep learning to generate hypotheses associated with biomarkers for specific neuro-psychiatric disorders. The use of machine learning to find biomarkers for epilepsy and adolescent mental disorders such as borderline personality disorder (BPD), using electroencephalogram (EEG), and functional magnetic resonance imaging (fMRI). Approaches for energy-efficient implementations for both traditional machine learning and deep learning systems such as feature ranking and incremental-precision for traditional machine learning systems and Perm-DNN based on permuted-diagonal interconnections for deep convolutional neural networks. 

AI/ML

Lesson Synopsis
This lesson will cover the design strategies of patient-specific wearable healthcare system System-on-Chip (SoC). We will begin with the difficulties and limitations in wearable environments. We will then study the IA circuit topologies and their key metrics to overcome such issues. Moving on, we will cover the feature extraction and the patient-specific classification using Machine Learning technique. Finally, an on-chip epilepsy detection and recording sensor SoC will be presented, which integrates all the components covered during the lecture. The lesson will conclude with interesting aspects and opportunities that lie ahead.
 

AI/ML

Lesson Synopsis

In recent years, many remarkable achievements have been made in the field of machine learning. Algorithms such as Monte Carlo Markov Chain and Monte Carlo Tree Search have been successfully used in the design of MIMO (i.e., multiple antenna) transceivers. In addition, highly quantized implementations, such as binarized networks, have led to implementations that are well-suited to power-limited mobile platforms. In addition, metaheuristic optimization techniques such the genetic algorithm and others have been used to automatically find highly efficient deep learning architectures. This lesson will describe these approaches and present some recent design examples.

AI/ML