Article
Telecommunications
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Summary: Spiking Neural Networks (SNNs) are biologically inspired machine learning models that process binary and sparse spiking signals. They can be implemented on energy-efficient neuromorphic computing platforms and have been validated for their capabilities in detecting and generating spatial patterns through experiments.
IEEE COMMUNICATIONS LETTERS
(2021)
Article
Chemistry, Multidisciplinary
Sofie J. Studholme, Zachary E. Heywood, Joshua B. Mallinson, Jamie K. Steel, Philip J. Bones, Matthew D. Arnold, Simon A. Brown
Summary: This study investigates the computational capability of percolating networks of nanoparticles (PNNs) based on brain-like criticality. By manipulating the spiking activity, PNNs are able to perform Boolean operations and image classification with near perfect accuracy. The key to successful computation lies in the powerful modulus-like nonlinearity of nanoscale tunnel gaps within the PNNs.
Article
Telecommunications
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Summary: The synergies between wireless communications and artificial intelligence are increasingly driving research at the intersection of the two fields. Machine learning can address algorithm and model deficits in the optimization of communication protocols, but implementing ML models on devices connected via bandwidth-constrained channels remains challenging.
IEEE COMMUNICATIONS LETTERS
(2021)
Article
Telecommunications
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Summary: This paper introduces the operation principle, training algorithms, and models of Spiking Neural Networks (SNNs) and compares two main methods. To address the non-differentiability of the spiking mechanism, a differentiable function approximating the threshold activation function is proposed, and an alternative method based on probability models is discussed. Finally, experimental results on accuracy and convergence under different SNN models are provided.
IEEE COMMUNICATIONS LETTERS
(2021)
Article
Engineering, Multidisciplinary
Saurabh Balkrishna Tandale, Marcus Stoffel
Summary: The present study aims to introduce an AI algorithm suitable for neuromorphic computing to solve Boundary Value Problems in Engineering Mechanics. By using Spiking Neural Networks (SNNs), the study proposes a surrogate model for mechanical tasks that is more energy-efficient than traditional neural networks. The researchers also propose a hybrid model that combines spiking recurrent cells, the spiking variant of the Legendre Memory Unit (LMU), and classical dense transformations to compute the nonlinear response of shock wave-loaded plate elements.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Hoyeon Cho, Donghyun Lee, Kyungmin Ko, Der-Yuh Lin, Huimin Lee, Sangwoo Park, Beomsung Park, Byung Chul Jang, Dong-Hyeok Lim, Joonki Suh
Summary: Two-dimensional materials and their heterostructures, such as van der Waals (vdW) integrated synaptic transistors, offer advantages such as near-atom thickness, superior electrostatic control, and adjustable device architecture. In this study, a double-floating-gate (DFG) device with multistacked floating gates was developed, showcasing improved nonvolatile memory performance and effective modulation of trapped charge density. The DFG transistor demonstrated improved weight update profile for long-term potentiation/depression synaptic behavior, achieving high classification accuracies in neural network tasks.
Article
Optics
Chenguang Zhu, Huawei Liu, Wenqiang Wang, Li Xiang, Jie Jiang, Qin Shuai, Xin Yang, Tian Zhang, Biyuan Zheng, Hui Wang, Dong Li, Anlian Pan
Summary: In this study, a low-power BP/CdS heterostructure-based artificial photonic synapse is proposed, and a fully-connected optoelectronic neural network with high image recognition accuracy is simulated. This research provides a new concept for designing energy-efficient artificial photonic synapses and demonstrates their great potential in high-performance neuromorphic vision systems.
LIGHT-SCIENCE & APPLICATIONS
(2022)
Article
Chemistry, Analytical
Jianwei Xue, Lisheng Xie, Faquan Chen, Liangshun Wu, Qingyang Tian, Yifan Zhou, Rendong Ying, Peilin Liu
Summary: Spiking neural networks (SNNs) are ideal for edge computing scenarios due to their intelligent features and energy efficiency. However, current mapping schemes for deploying SNNs onto neuromorphic hardware face limitations such as extended execution times and low throughput. To address these challenges, EdgeMap is introduced as an optimized mapping toolchain specifically designed for efficient deployment of SNNs onto edge devices.
Article
Chemistry, Analytical
Ling Zhang, Jing Yang, Cong Shi, Yingcheng Lin, Wei He, Xichuan Zhou, Xu Yang, Liyuan Liu, Nanjian Wu
Summary: Neuromorphic hardware systems utilizing spiking neural networks have gained attention for embedded applications due to their brain-inspired, energy-efficient design. This paper introduces a novel VLSI architecture for accelerating deep SCNN inference in real-time low-cost embedded scenarios, achieving high processing speed and recognition accuracies on image datasets. The architecture leverages snapshot processing and fine-grained data pipelines to achieve high throughput, demonstrating the feasibility of SCNN hardware for various embedded applications.
Article
Computer Science, Software Engineering
Kicheol Park, Bongjae Kim
Summary: With the advancement of IoT and AI technologies, intelligent IoT services are gaining popularity. To enable intelligent functions in resource-constrained IoT devices, the use of low-power neuromorphic computing devices/architectures is proposed. A model called Neuromorphic Architecture Abstraction (NAA) dynamically selects the appropriate architecture based on parameter size, specifications, and error probability. Experimental results demonstrate that the proposed NAA model reduces training and inferencing time compared to random architecture selection.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Luca Zanatta, Alfio Di Mauro, Francesco Barchi, Andrea Bartolini, Luca Benini, Andrea Acquaviva
Summary: This paper presents an energy-efficient implementation of a Reinforcement Learning algorithm using Spiking Neural Networks for obstacle avoidance task on an Unmanned Aerial Vehicle. The SNN algorithm achieves better results than a Convolutional Neural Network, with 6x less energy consumption.
Article
Chemistry, Physical
Dongyeol Ju, Minsuk Koo, Sungjun Kim, Toma Stoica
Summary: This paper investigates the bipolar resistive switching and synaptic characteristics of IZO single-layer and IZO/SiO2 bilayer two-terminal memory devices. The results show that the device with the SiO2 layer has a lower current level and better uniformity, and exhibits favorable abilities in neuromorphic applications.
Article
Chemistry, Multidisciplinary
Fan Ye, Fatemeh Kiani, Yi Huang, Qiangfei Xia
Summary: This research improves the uniformity of relaxation time in diffusive memristors by engineering the device stack, and achieves tunability in relaxation time. An algorithm utilizing the tunable and uniform relaxation behavior for spike generation is implemented, and achieves high accuracy in object recognition.
ADVANCED MATERIALS
(2023)
Review
Chemistry, Multidisciplinary
Hefei Liu, Yuan Qin, Hung-Yu Chen, Jiangbin Wu, Jiahui Ma, Zhonghao Du, Nan Wang, Jingyi Zou, Sen Lin, Xu Zhang, Yuhao Zhang, Han Wang
Summary: This paper reviews the progress of artificial neuronal devices based on emerging volatile switching materials, focusing on the demonstrated neuron models implemented in these devices and their utilization for computational and sensing applications. Furthermore, it discusses the inspirations from neuroscience and engineering methods to enhance the neuronal dynamics that are yet to be realized in artificial neuronal devices and networks towards achieving the full functionalities of biological neurons.
ADVANCED MATERIALS
(2023)
Article
Computer Science, Artificial Intelligence
Junxiu Liu, Dong Jiang, Yuling Luo, Senhui Qiu, Yongchuang Huang
Summary: This study introduces a minimally buffered deflection router (MBDR) to address the scalability challenge of hardware SNNs, which reduces energy consumption and costs while maintaining a high level of system throughput through the use of deflection router technique and a novel flow controller.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Chemistry, Analytical
Jordi Fonollosa, Sadique Sheik, Ramon Huerta, Santiago Marco
SENSORS AND ACTUATORS B-CHEMICAL
(2015)
Editorial Material
Neurosciences
Christian G. Mayr, Sadique Sheik, Chiara Bartolozzi, Elisabetta Chicca
FRONTIERS IN NEUROSCIENCE
(2016)
Article
Neurosciences
Hesham Mostafa, Bruno Pedroni, Sadique Sheik, Gert Cauwenberghs
FRONTIERS IN NEUROSCIENCE
(2017)
Article
Neurosciences
Georgios Detorakis, Sadique Sheik, Charles Augustine, Somnath Paul, Bruno U. Pedroni, Nikil Dutt, Jeffrey Krichmar, Gert Cauwenberghs, Emre Neftci
FRONTIERS IN NEUROSCIENCE
(2018)
Article
Neurosciences
Bruno U. Pedroni, Siddharth Joshi, Stephen R. Deissl, Sadique Sheik, Georgios Detorakis, Somnath Paul, Charles Augustine, Emre O. Neftci, Gert Cauwenberghs
FRONTIERS IN NEUROSCIENCE
(2019)
Proceedings Paper
Computer Science, Artificial Intelligence
Qian Liu, Ole Richter, Carsten Nielsen, Sadique Sheik, Giacomo Indiveri, Ning Qiao
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2019)
(2019)
Proceedings Paper
Computer Science, Information Systems
Bruno U. Pedroni, Sadique Sheik, Hesham Mostafa, Somnath Paul, Charles Augustine, Gert Cauwenberghs
2018 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS): ADVANCED SYSTEMS FOR ENHANCING HUMAN HEALTH
(2018)
Proceedings Paper
Computer Science, Information Systems
Surabhi Kalyan, Siddharth Joshi, Sadique Sheik, Bruno U. Pedroni, Gert Cauwenberghs
2018 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS): ADVANCED SYSTEMS FOR ENHANCING HUMAN HEALTH
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Sadique Sheik
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON APPLICATIONS IN NONLINEAR DYNAMICS (ICAND 2016)
(2017)
Proceedings Paper
Engineering, Electrical & Electronic
Bruno U. Pedroni, Sadique Sheik, Gert Cauwenberghs
2017 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
(2017)
Proceedings Paper
Computer Science, Information Systems
Bruno U. Pedroni, Sadique Sheik, Siddharth Joshi, Georgios Detorakis, Somnath Paul, Charles Augustine, Emre Neftci, Gert Cauwenberghs
PROCEEDINGS OF 2016 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS)
(2016)
Proceedings Paper
Computer Science, Information Systems
Sadique Sheik, Somnath Paul, Charles Augustine, Gert Cauwenberghs
PROCEEDINGS OF 2016 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS)
(2016)
Proceedings Paper
Engineering, Electrical & Electronic
Sadique Sheik, Somnath Paul, Charles Augustine, Chinnikrishna Kothapalli, Muhammad M. Khellah, Gert Cauwenberghs, Emre Neftci
2016 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
(2016)