arXiv subjects

Morteza Hosseini

Publications and source records attributed to Morteza Hosseini.

4 recordsLinked to original sources

arXiv2022

2022 Roadmap on Neuromorphic Computing and Engineering

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83

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arXiv2021

The chemical space of terpenes: insights from data science and AI

Terpenes are a widespread class of natural products with significant chemical and biological diversity and many of these molecules have already made their way into medicines. Given the thousands of molecules already described, the full characterization of this chemical space can be a challenging task when relying in classical approaches. In this work we employ a data science-based approach to identify, compile and characterize the diversity of terpenes currently known in a systematic way. We worked with a natural product database, COCONUT, from which we extracted information for nearly 60000 terpenes. For these molecules, we conducted a subclass-by-subclass analysis in which we highlight several chemical and physical properties relevant to several fields, such as natural products chemistry, medicinal chemistry and drug discovery, among others. We were also interested in assessing the potential of this data for clustering and classification tasks. For clustering, we have applied and compared k-means with agglomerative clustering, both to the original data and following a step of dimensionality reduction. To this end, PCA, FastICA, Kernel PCA, t-SNE and UMAP were used and benchmarked. We also employed a number of methods for the purpose of classifying terpene subclasses using their physico-chemical descriptors. Light gradient boosting machine, k-nearest neighbors, random forests, Gaussian naiive Bayes and Multilayer perceptron, with the best-performing algorithms yielding accuracy, F1 score, precision and other metrics all over 0.9, thus showing the capabilities of these approaches for the classification of terpene subclasses.

cs.LG↗

arXiv2020

Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information

Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the machine-aided diagnosis of respiratory problems. This paper is thus an effort to exploit machine learning for classification of respiratory problems and proposes a framework that employs as much correlated information (auditory and demographic information in this work) as a dataset provides to increase the sensitivity and specificity of a diagnosing system. First, we use deep convolutional neural networks (DCNNs) to process and classify a publicly released pulmonary auditory dataset, and then we take advantage of the existing demographic information within the dataset and show that the accuracy of the pulmonary classification increases by 5% when trained on the auditory information in conjunction with the demographic information. Since the demographic data can be extracted using computer vision, we suggest using another parallel DCNN to estimate the demographic information of the subject under test visioned by the processing computer. Lastly, as a proposition to bring the healthcare system to users' fingertips, we measure deployment characteristics of the auditory DCNN model onto processing components of an NVIDIA TX2 development board.

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arXiv2019

Minimizing Classification Energy of Binarized Neural Network Inference for Wearable Devices

In this paper, we propose a low-power hardware for efficient deployment of binarized neural networks (BNNs) that have been trained for physiological datasets. BNNs constrain weights and feature-map to 1 bit, can pack in as many 1-bit weights as the width of a memory entry provides, and can execute multiple multiply-accumulate (MAC) operations with one fused bit-wise xnor and population-count instruction over aligned packed entries. Our proposed hardware is scalable with the number of processing engines (PEs) and the memory width, both of which adjustable for the most energy efficient configuration given an application. We implement two real case studies including Physical Activity Monitoring and Stress Detection on our platform, and for each case study on the target platform, we seek the optimal PE and memory configurations. Our implementation results indicate that a configuration with a good choice of memory width and number of PEs can be optimized up to 4x and 2.5x in energy consumption respectively on Artix-7 FPGA and on 65nm CMOS ASIC implementation. We also show that, generally, wider memories make more efficient BNN processing hardware. To further reduce the energy, we introduce Pool-Skipping technique that can skip at least 25% of the operations that are accompanied by a Max-Pool layer in BNNs, leading to a total of 22% operation reduction in the Stress Detection case study. Compared to the related works using the same case studies on the same target platform and with the same classification accuracy, our hardware is respectively 4.5x and 250x more energy efficient for the Stress Detection on FPGA and Physical Activity Monitoring on ASIC, respectively.

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