Unlocking the Potential of Two-Point Cells for Energy-Efficient and Resilient Training of Deep Nets

Ahsan Adeel, Adewale Adetomi, Khubaib Ahmed, Amir Hussain, Tughrul Arslan, W. A. Phillips

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as 1999. However, the potential of this discovery to provide useful neural computation has yet to be demonstrated. Here we show for the first time how a transformative L5PCs-driven deep neural network (DNN), termed the multisensory cooperative computing (MCC) architecture, can effectively process large amounts of heterogeneous real-world audio-visual (AV) data, using far less energy compared to best available 'point' neuron-driven DNNs. A novel highly-distributed parallel implementation on a Xilinx UltraScale+ MPSoC device estimates energy savings up to 245759 × 50000 μJ (i.e., 62% less than the baseline model in a semi-supervised learning setup) where a single synapse consumes 8e-5μJ. In a supervised learning setup, the energy-saving can potentially reach up to 1250x less (per feedforward transmission) than the baseline model. The significantly reduced neural activity in MCC leads to inherently fast learning and resilience against sudden neural damage. This remarkable performance in pilot experiments demonstrates the embodied neuromorphic intelligence of our proposed cooperative L5PC that receives input from diverse neighbouring neurons as context to amplify the transmission of most salient and relevant information for onward transmission, from overwhelmingly large multimodal information utilised at the early stages of on-chip training. Our proposed approach opens new cross-disciplinary avenues for future on-chip DNN training implementations and posits a radical shift in current neuromorphic computing paradigms.

Original languageEnglish
Article number10057134
Pages (from-to)818-828
Number of pages11
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number3
Early online date1 Mar 2023
Publication statusPublished - 1 Jun 2023

Keywords / Materials (for Non-textual outputs)

  • Adders
  • Computer architecture
  • Context-sensitive neuron
  • Hardware
  • Neurons
  • Radio frequency
  • Task analysis
  • Training
  • deep network
  • neuromorphic computing
  • training


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