Visualizing Transaction-Level Modeling Simulations of Deep Neural Networks

The growing complexity of data-intensive software demands constant innovation in computer hardware design. Performance is a critical factor in rapidly evolving applications such as artificial intelligence (AI). Transaction-level modeling (TLM) is a valuable technique used to represent hardware and software behavior in a simulated environment. However, extracting actionable insights from TLM simulations is not a trivial task. We present Netmemvisual, an interactive, cross-platform visualization tool for exposing memory bottlenecks in TLM simulations. We demonstrate how Netmemvisual helps system designers rapidly analyze complex TLM simulations to find memory contention. We describe the project’s current features, experimental results with two state-of-the-art deep neural networks (DNNs), and planned future work

Unobtrusive cot side sleep stage classification in preterm infants using ultra-wideband radar

Background: Sleep is an important driver of development in infants born preterm. However, continuous unobtrusive sleep monitoring of infants in the neonatal intensive care unit (NICU) is challenging.Objective: To assess the feasibility of ultra-wideband (UWB) radar for sleep stage classification in preterm infants admitted to the NICU.Methods: Active and quiet sleep were visually assessed using video recordings in 10 preterm infants (recorded between 29 and 34 weeks of postmenstrual age) admitted to the NICU. UWB radar recorded all infant's motions during the video recordings. From the baseband data measured with the UWB radar, a total of 48 features were calculated. All features were related to body and breathing movements. Six machine learning classifiers were compared regarding their ability to reliably classify active and quiet sleep using these raw signals.Results: The adaptive boosting (AdaBoost) classifier achieved the highest balanced accuracy (81%) over a 10-fold cross-validation, with an area under the curve of receiver operating characteristics (AUC-ROC) of 0.82.Conclusions: The UWB radar data, using the AdaBoost classifier, is a promising method for non-obtrusive sleep stage assessment in very preterm infants admitted to the NICU

Ultra-wideband radar for simultaneous and unobtrusive monitoring of respiratory and heart rates in early childhood:A Deep Transfer Learning Approach

Unobtrusive monitoring of children’s heart rate (HR) and respiratory rate (RR) can be valuable for promoting the early detection of potential health issues, improving communication with healthcare providers and reducing unnecessary hospital visits. A promising solution for wireless vital sign monitoring is radar technology. This paper presents a novel approach for the simultaneous estimation of children’s RR and HR utilizing ultra-wideband (UWB) radar using a deep transfer learning algorithm in a cohort of 55 children. The HR and RR are calculated by processing radar signals via spectrogram from time epochs of 10 s (25 sample length of hamming window with 90% overlap) and then transforming the resultant representation into 2-dimensional images. These images were fed into a pre-trained Visual Geometry Group-16 (VGG-16) model (trained on ImageNet dataset), with weights of five added layers fine-tuned using the proposed data. The prediction on the test data achieved a mean absolute error (MAE) of 7.3 beats per minute (BPM &lt; 6.5% of average HR) and 2.63 breaths per minute (BPM &lt; 7% of average RR). We also achieved a significant Pearson’s correlation of 77% and 81% between true and extracted for HR and RR, respectively. HR and RR samples are extracted every 10 s.</p

Transaction-Level Modeling of Deep Neural Networks for Efficient Parallelism and Memory Accuracy

The emergence of data-intensive applications, such as Deep Neural Networks (DNNs), exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using Transaction Level Modeling (TLM) enables early performance estimation, efficient design space exploration, and gradual refinement. In this dissertation, we present our exploratory modeling framework for hardware-software codesign based on IEEE SystemC TLM with particular focus on exposing parallelism and memory contention. We demonstrate the effectiveness of our approach for representative large DNNs such as GoogLeNet and Single Shot MultiBox Detector.First, we study the impact of communication mechanisms on the available parallelism in TLM models. Specifically, we demonstrate the impact of varying synchronization mechanisms and buffering schemes on the exposed parallelism using different modeling styles of a DNN. We measure the performance of aggressive out-of-order parallel discrete event simulation and analyze the available parallelism in the models. Our study suggests that increased parallel simulation performance indicates better models with higher amounts of parallelism exposed.Second, we explore the critical aspects of modeling and analysis of timing accuracy with the respect to memory contention. A major hurdle in tackling the memory bottleneck is the detection of memory contention late in the design cycle when detailed timed or cycle-accurate models are developed. A bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. To explore new architectures prior to RTL implementation, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models, yet shows the same accuracy for memory contention as low level approximately-timed (AT) models.Finally, we further refine the TLM-2.0 AT model by adding a cycle-accurate model of a memory subsystem. This model provides a higher timing accuracy for contention analysis. Hence it provides more accurate estimation of the performance. We revise our LT-CA memory delay modeling to provide further accuracy comparable to the cycle-accurate AT model of the shared memory subsystem. The high amount of contention on the shared memory suggests the need to move toward new processor architectures with local memories

Deep Transfer Learning for Parkinson’s Disease Monitoring by Image-Based Representation of Resting-State EEG Using Directional Connectivity

Parkinson’s disease (PD) is characterized by abnormal brain oscillations that can change rapidly. Tracking neural alternations with high temporal resolution electrophysiological monitoring methods such as EEG can lead to valuable information about alterations observed in PD. Concomitantly, there have been advances in the high-accuracy performance of deep neural networks (DNNs) using few-patient data. In this study, we propose a method to transform resting-state EEG data into a deep latent space to classify PD subjects from healthy cases. We first used a general orthogonalized directed coherence (gOPDC) method to compute directional connectivity (DC) between all pairwise EEG channels in four frequency bands (Theta, Alpha, Beta, and Gamma) and then converted the DC maps into 2D images. We then used the VGG-16 architecture (trained on the ImageNet dataset) as our pre-trained model, enlisted weights of convolutional layers as initial weights, and fine-tuned all layer weights with our data. After training, the classification achieved 99.62% accuracy, 100% precision, 99.17% recall, 0.9958 F1 score, and 0.9958 AUC averaged for 10 random repetitions of training/evaluating on the proposed deep transfer learning (DTL) network. Using the latent features learned by the network and employing LASSO regression, we found that latent features (as opposed to the raw DC values) were significantly correlated with five clinical indices routinely measured: left and right finger tapping, left and right tremor, and body bradykinesia. Our results demonstrate the power of transfer learning and latent space derivation for the development of oscillatory biomarkers in PD.Medicine, Faculty ofOther UBCNon UBCMedicine, Department ofNeurology, Division ofReviewedFacultyResearche