UoA Competitive Research Funding (Ref. 2024-24): Combination of Approximate Computing and Approximate Stacking Memory for Low-power Neuromorphic Computing (2024)

Brief description

In this research, we investigate designing a low-power neuromorphic computing solution for IoT and Edge devices by combining Approximate Computing and Approximate Stacking Memory. As IoT and edge devices have become ubiquitous, it is necessary to have the ability to calculate the neural networks on the devices using dedicated chips/modules. However, current neural network architectures are power intensive, especially for deep neural networks, which prevents them from being able to be widely adopted. As a result, there is a need for low-power solutions for Neuromorphic Computing. In this research proposal, we combine Approximate Stacking Memory with Approximate Computing to improve energy efficiency further while maintaining overall accuracy.

Output

  1. Ryoji Kobayashi and Khanh N. Dang, '’An Efficient Hardware Implementation of Spiking Neural Network Using Approximate Izhikevich Neuron’‘. 2024 9th IEEE International Conference on Integrated Circuits, Design, and Verification, June 6-8, 2024. [URL]
  2. Ngo-Doanh Nguyen, Khanh N. Dang, Akram Ben Ahmed, Abderazek Ben Abdallah, Xuan-Tu Tran, '’NOMA: A Novel Reliability Improvement Methodology for 3-D IC-based Neuromorphic Systems’‘, IEEE Transactions on Components, Packaging and Manufacturing Technology*, 2024.
  3. Yuga Hanyu and Khanh N. Dang, '’EnsembleSTDP: Distributed in-situ Spike Timing Dependent Plasticity Learning in Spiking Neural Networks’‘, 2024 IEEE 17th International Symposium on Embedded Multicore*Many-core Systems-on-Chip (MCSoC), Dec. 16-19, 2024.
  4. Ryoji Kobayashi, Ngo-Doanh Nguyen, Nguyen Anh Vu Doan and Khanh N. Dang, '’Energy-Efficient Spiking Neural Networks Using Approximate Neuron Circuits and 3D Stacking Memory’‘, 2024 IEEE 17th International Symposium on Embedded Multicore*Many-core Systems-on-Chip (MCSoC), Dec. 16-19, 2024.