Conference proceeding
Towards Biology-Inspired Fault Tolerance of Neuromorphic Hardware for Space Applications
2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), pp 1-7
08 Oct 2024
Abstract
High-energy particles in space can induce single-and multibit upsets in random electronic components of FPGA-based neuromorphic systems. We propose NeuFT, a low overhead biology-inspired architecture for fault tolerance of these systems. NeuFT draws inspiration from astrocytes, which are star-shaped glial cells in a mammalian brain that facilitate the self-repair of failed neurons by sending a closed-loop retrograde feedback signal. Our fault-tolerant design methodology is as follows. First, we partition a spiking neural network (SNN) model into synaptic islands and place an astrocyte in each of them. Each astrocyte is trained using spike trains of neurons on its synaptic island to optimize fault tolerance capabilities. Next, we propose a practical astrocyte-inspired controller that integrates astrocyte feedback as bias of a leaky integrate and fire neuron with minimal design changes. Finally, we introduce software to partition and train an astrocyte-enabled SNN and map it to hardware. We evaluate NeuFT for five datasets with varying degrees of bit error rates considering single- and multibit upsets. Our results show a significant reduction in fault tolerance overhead while delivering a high classification accuracy with respect to state-of-the-art.
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Details
- Title
- Towards Biology-Inspired Fault Tolerance of Neuromorphic Hardware for Space Applications
- Creators
- Shadi Matinizadeh - Drexel UniversitySarah Johari - Drexel UniversityArghavan Mohammadhassani - Drexel UniversityAnup Das - Drexel University
- Publication Details
- 2024 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), pp 1-7
- Publisher
- IEEE
- Grant note
- CCF-1942697 / United States NSF (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Computer Science (Computing); College of Engineering
- Scopus ID
- 2-s2.0-85212433363
- Other Identifier
- 991021965371304721