ArticleFrontiers in neuroscience2018
An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Low-latency hierarchical routing of reconfigurable neuromorphic systems.Frontiers in neuroscience · 2025Article
- Memory-efficient neurons and synapses for spike-timing-dependent-plasticity in large-scale spiking networks.Frontiers in neuroscience · 2024Article
- Trends and Challenges in AIoT/IIoT/IoT Implementation.Sensors (Basel, Switzerland) · 2023Article
- neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time.Frontiers in computational neuroscience · 2023Article
- A System-on-Chip Based Hybrid Neuromorphic Compute Node Architecture for Reproducible Hyper-Real-Time Simulations of Spiking Neural Networks.Frontiers in neuroinformatics · 2022Article
- A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware.Scientific reports · 2021Article
- Sharpness recognition based on synergy between bio-inspired nociceptors and tactile mechanoreceptors.Scientific reports · 2021Article
- Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs.Frontiers in computational neuroscience · 2021Article
- Supervised Learning in All FeFET-Based Spiking Neural Network: Opportunities and Challenges.Frontiers in neuroscience · 2020Article
- A Digital Hardware System for Spiking Network of Tactile Afferents.Frontiers in neuroscience · 2019Article
- Scalable Method to Find the Shortest Path in a Graph with Circuits of Memristors.Physical review applied · 2018Article
- Code Generation in Computational Neuroscience: A Review of Tools and Techniques.Frontiers in neuroinformatics · 2018Review
- Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain.Frontiers in neuroscience · 2018Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.
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