Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System
Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-06-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.690208/full |
| _version_ | 1818437590989144064 |
|---|---|
| author | Abderazek Ben Abdallah Khanh N. Dang Khanh N. Dang |
| author_facet | Abderazek Ben Abdallah Khanh N. Dang Khanh N. Dang |
| author_sort | Abderazek Ben Abdallah |
| collection | DOAJ |
| description | Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, although fault-tolerance is a natural feature of biological systems, integrating many computation and memory units into neuromorphic chips confronts the reliability issue, where a defective part can affect the overall system's performance. This paper presents the design and simulation of R-NASH-a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure, where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent-plasticity rule. Our platform enables high integration density and small spike delay of spiking networks and features a scalable design. R-NASH is a design based on the Through-Silicon-Via technology, facilitating spiking neural network implementation on clustered neurons based on Network-on-Chip. We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. Moreover, R-NASH supports fault recovery with graceful performance degradation. |
| first_indexed | 2024-12-14T17:27:06Z |
| format | Article |
| id | doaj.art-7ae373bfeaa44a5ebfbe5282fbd13b5a |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-14T17:27:06Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-7ae373bfeaa44a5ebfbe5282fbd13b5a2022-12-21T22:53:11ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-06-011510.3389/fnins.2021.690208690208Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm SystemAbderazek Ben Abdallah0Khanh N. Dang1Khanh N. Dang2Adaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, JapanAdaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, JapanVNU Key Laboratory for Smart Integrated Systems (SISLAB), VNU University of Engineering and Technology, Vietnam National University, Hanoi, VietnamSpiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, although fault-tolerance is a natural feature of biological systems, integrating many computation and memory units into neuromorphic chips confronts the reliability issue, where a defective part can affect the overall system's performance. This paper presents the design and simulation of R-NASH-a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure, where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent-plasticity rule. Our platform enables high integration density and small spike delay of spiking networks and features a scalable design. R-NASH is a design based on the Through-Silicon-Via technology, facilitating spiking neural network implementation on clustered neurons based on Network-on-Chip. We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. Moreover, R-NASH supports fault recovery with graceful performance degradation.https://www.frontiersin.org/articles/10.3389/fnins.2021.690208/fullspiking neural networkneuromorphic3D-ICsfault-tolerancemapping algorithm |
| spellingShingle | Abderazek Ben Abdallah Khanh N. Dang Khanh N. Dang Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System Frontiers in Neuroscience spiking neural network neuromorphic 3D-ICs fault-tolerance mapping algorithm |
| title | Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System |
| title_full | Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System |
| title_fullStr | Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System |
| title_full_unstemmed | Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System |
| title_short | Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System |
| title_sort | toward robust cognitive 3d brain inspired cross paradigm system |
| topic | spiking neural network neuromorphic 3D-ICs fault-tolerance mapping algorithm |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.690208/full |
| work_keys_str_mv | AT abderazekbenabdallah towardrobustcognitive3dbraininspiredcrossparadigmsystem AT khanhndang towardrobustcognitive3dbraininspiredcrossparadigmsystem AT khanhndang towardrobustcognitive3dbraininspiredcrossparadigmsystem |