An FPGA-based Massively Parallel Neuromorphic Cortex Simulator

Article, Preprint English OPEN
Wang, Runchun ; Thakur, Chetan Singh ; van Schaik, Andre (2018)
  • Publisher: Frontiers Media S.A.
  • Journal: Frontiers in Neuroscience, volume 12 (issn: 1662-4548, eissn: 1662-453X)
  • Related identifiers: pmc: PMC5902707, doi: 10.3389/fnins.2018.00213, doi: 10.3389/fnins.2018.00213/full
  • Subject: neuromorphic engineering | Neurosciences. Biological psychiatry. Neuropsychiatry | RC321-571 | Neuroscience | spiking neural networks | neocortex | Original Research | computational neuroscience | stochastic computing | Computer Science - Neural and Evolutionary Computing

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 200k 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 {\mu}W 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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