Neural Processing Letters

, Volume 38, Issue 2, pp 131–153 | Cite as

Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network

  • Sandeep PandeEmail author
  • Fearghal Morgan
  • Seamus Cawley
  • Tom Bruintjes
  • Gerard Smit
  • Brian McGinley
  • Snaider Carrillo
  • Jim Harkin
  • Liam McDaid


Biologically-inspired packet switched network on chip (NoC) based hardware spiking neural network (SNN) architectures have been proposed as an embedded computing platform for classification, estimation and control applications. Storage of large synaptic connectivity (SNN topology) information in SNNs require large distributed on-chip memory, which poses serious challenges for compact hardware implementation of such architectures. Based on the structured neural organisation observed in human brain, a modular neural networks (MNN) design strategy partitions complex application tasks into smaller subtasks executing on distinct neural network modules, and integrates intermediate outputs in higher level functions. This paper proposes a hardware modular neural tile (MNT) architecture that reduces the SNN topology memory requirement of NoC-based hardware SNNs by using a combination of fixed and configurable synaptic connections. The proposed MNT contains a 16:16 fully-connected feed-forward SNN structure and integrates in a mesh topology NoC communication infrastructure. The SNN topology memory requirement is 50 % of the monolithic NoC-based hardware SNN implementation. The paper also presents a lookup table based SNN topology memory allocation technique, which further increases the memory utilisation efficiency. Overall the area requirement of the architecture is reduced by an average of 66 % for practical SNN application topologies. The paper presents micro-architecture details of the proposed MNT and digital neuron circuit. The proposed architecture has been validated on a Xilinx Virtex-6 FPGA and synthesised using 65 nm low-power CMOS technology. The evolvable capability of the proposed MNT and its suitability for executing subtasks within a MNN execution architecture is demonstrated by successfully evolving benchmark SNN application tasks representing classification and non-linear control functions. The paper addresses hardware modular SNN design and implementation challenges and contributes to the development of a compact hardware modular SNN architecture suitable for embedded applications


Modular neural networks (MNN) Spiking neural networks (SNN)  Synaptic connectivity Network on chip (NoC) EMBRACE 



This research is supported by International Centre for Graduate Education in Micro and Nano-Engineering (ICGEE), Irish Research Council for Science, Engineering and Technology (IRCSET), Science Foundation, Ireland (Grant No. 07/SRC/I1169) and Xilinx University Programme.


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Sandeep Pande
    • 1
    Email author
  • Fearghal Morgan
    • 1
  • Seamus Cawley
    • 1
  • Tom Bruintjes
    • 2
  • Gerard Smit
    • 2
  • Brian McGinley
    • 1
  • Snaider Carrillo
    • 3
  • Jim Harkin
    • 3
  • Liam McDaid
    • 3
  1. 1.Bio-Inspired Electronics and Reconfigurable ComputingNational University of IrelandGalwayIreland
  2. 2.Computer Architecture for Embedded SystemsUniversity of TwenteEnschedeThe Netherlands
  3. 3.Intelligent Systems Research CentreUniversity of UlsterDerryNorthern Ireland, UK

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