Neuromorphic Computing and Brain-Inspired Systems

NASH: Neuromorphic Adaptive System Hardware

We are pioneering the development of the NASH chip, an ultra-low-power processing unit designed for real-time cognitive tasks. This research focuses on the integration of 3D Network-on-Chip (NoC) interconnects to overcome the data-movement bottlenecks of conventional 2D architectures. By utilizing adaptive configuration methods, the NASH system allows for on-the-fly adjustment of Spiking Neural Network (SNN) parameters, including synaptic weights and neuronal thresholds. This flexibility enables the hardware to self-optimize for diverse biological signals, bringing high-performance brain-inspired processing to edge-level BCI devices.

ApproxiMorph exploits the inherent noise resilience of Spiking Neural Networks by implementing layer-wise approximation strategies within 3D-stacked IC designs. The framework integrates approximate neuron designs with low-voltage 3D-stacked SRAM to significantly reduce the power-per-synaptic operation. By utilizing a heuristic search algorithm to navigate a massive configuration space (over 10¹⁷ options), the system identifies optimal mappings that achieve nearly 30% power reduction with almost no impact on classification performance.

The BTSAM framework introduces a periodic activity scoring system to monitor the workload of 3D-NoC nodes. By applying Seesaw Neuron Clustering (SNC) and a thermal-aware genetic algorithm, the system dynamically redistributes neuronal tasks to eliminate hot spots. This proactive thermal management prevents structural damage and results in a 5K peak temperature reduction and a four-fold increase in Mean-Time-to-Failure (MTTF).

This research tackles the "memory wall" by decoupling synaptic weight storage from processing elements through vertical stacking. By splitting synaptic weights into significant and least-significant subsets across different 3D layers, the system can independently control the supply voltage. This allows for power-gating of memory layers containing less critical data, achieving SOP energy reductions of up to 66%.

R-MaS3N is a mapping framework designed to maintain system integrity in the presence of physical manufacturing defects. When a hardware fault is detected, R-MaS3N facilitates rapid remapping of SNN layers to functional neuronal clusters. The system maintains 100% efficiency even when 40% of hardware nodes are faulty, reducing remapping time by 71 times compared to conventional methods.

• [Patent No. 7699791] (June 20, 2025) - Abderazek Ben Abdallah, Hoang Huang Kun, Dang Nam Khanh, Song Janning, "AI Processor" [Source]
• [Patent No. 7277682] (May 11, 2023) - Abderazek Ben Abdallah, The H. Vu, Masayuki Hisada, "Spiking Neural Network by 3D NoC" [Google Patents]
• Neural Network Processor (Provisional 2024-047372) - Khanh N. Dang, Abderazek Ben Abdallah, Nguyen Ngo Doanh

• Ryoji Kobayashi, Ngo-Doanh Nguyen, Abderazek Ben Abdallah, Nguyen Anh Vu Doan and Khanh N. Dang, "ApproxiMorph: Energy-efficient Neuromorphic System with Layer-wise Approximation of Spiking Neural Networks and 3D-Stacked SRAM", in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, doi: 10.1109/TCAD.2025.3597251.
• N. -D. Nguyen, K. N. Dang, A. B. Ahmed, A. Ben Abdallah and X. -T. Tran, "NOMA: A Novel Reliability Improvement Methodology for 3-D IC-based Neuromorphic Systems," in IEEE Transactions on Components, Packaging and Manufacturing Technology, doi: 10.1109/TCPMT.2024.3488113.
• M. Maatar, Z. Wang, K. N. Dang and A. Ben Abdallah, "BTSAM: Balanced Thermal-State-Aware Mapping Algorithms and Architecture for 3D-NoC-Based Neuromorphic Systems," in IEEE Access, vol. 12, pp. 126679-126692, 2024, doi: 10.1109/ACCESS.2024.3425900.
• K. N. Dang, N. A. V. Doan, N. -D. Nguyen and A. Ben Abdallah, "HeterGenMap: An Evolutionary Mapping Framework for Heterogeneous NoC-Based Neuromorphic Systems," in IEEE Access, vol. 11, pp. 144095-144112, 2023, doi: 10.1109/ACCESS.2023.3345168.
• N. -D. Nguyen, A. B. Ahmed, A. Ben Abdallah and K. N. Dang, "Power-Aware Neuromorphic Architecture With Partial Voltage Scaling 3-D Stacking Synaptic Memory," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 31, no. 12, pp. 2016-2029, Dec. 2023, doi: 10.1109/TVLSI.2023.3318231.
• W. Y. Yerima, K. N. Dang and A. Ben Abdallah, "R-MaS3N: Robust Mapping of Spiking Neural Networks to 3D-NoC-Based Neuromorphic Systems for Enhanced Reliability," in IEEE Access, vol. 11, pp. 94664-94678, 2023, doi: 10.1109/ACCESS.2023.3311031.
• N. -D. Nguyen, X. -T. Tran, A. Ben. Abdallah and K. N. Dang, "An In-Situ Dynamic Quantization With 3D Stacking Synaptic Memory for Power-Aware Neuromorphic Architecture," in IEEE Access, vol. 11, pp. 82377-82389, 2023, doi: 10.1109/ACCESS.2023.3301560.
• O. M. Ikechukwu, K. N. Dang and A. B. Abdallah, "On the Design of a Fault-Tolerant Scalable Three Dimensional NoC-Based Digital Neuromorphic System With On-Chip Learning," in IEEE Access, vol. 9, pp. 64331-64345, 2021
• K. N. Dang, A. B. Ahmed, A. B. Abdallah and X. -T. Tran, "TSV-OCT: A Scalable Online Multiple-TSV Defects Localization for Real-Time 3-D-IC Systems," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 3, pp. 672-685, March 2020
• Ben Abdallah A, Dang KN. Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System. Front Neurosci. 2021 Jun 25;15:690208. doi: 10.3389/fnins.2021.690208.
• Ogbodo Mark Ikechukwu, Khanh N. Dang and Abderazek Ben Abdallah, "Energy-efficient Spike-based Scalable Architecture for Next-generation Cognitive AI Computing Systems," Springer Lecture Note in Computer Science (LNCS), International Symposium on Ubiquitous Networking 2021 (UNET21), May 19 ? May 22, 2021, Marakesh, Morocco (Best Student Paper Award).
• The H. Vu, Yuichi Okuyama, Abderazek Ben Abdallah, "Analytical performance assessment and high-throughput low-latency spike routing algorithm for spiking neural network systems," Journal of Supercomputing 75, pp. 5367?5397 (2019)
• Ogbodo Mark Ikechukwu, Khanh N. Dang, Tomohide Fukuchi, Abderazek Ben Abdallah, "Architecture and Design of a Spiking Neuron Processor Core Towards the Design of a Large-scale Event-Driven 3D-NoC-based Neuromorphic Processor," ACM Chapter International Conference on Educational Technology, Language and Technical Communication (ETLTC), January 27-31, 2020, Aizuwakamatsu, Japan.
• Mark Ogbodo, The Vu, Khanh N. Dang and Abderazek Abdallah, "Light-weight Spiking Neuron Processing Core for Large-scale 3D-NoC based Spiking Neural Network Processing Systems," The 7th IEEE International Conference on Big Data and Smart Computing, February 19-22, 2020, Pusan, South Korea.
• Khanh N. Dang and Abderazek Ben Abdallah "An Efficient Software-Hardware Design Framework for Spiking Neural Network Systems," 2019 IEEE International Conference on Internet of Things, Embedded Systems and Communications (IINTEC 2019), Tunis, Tunisia, 2019, pp. 155-162.
• The H. Vu, Abderazek Ben Abdallah, "A Low-latency K-means based Multicast Routing Algorithm and Architecture for Three Dimensional Spiking Neuromorphic Chips," IEEE International Conference on Big Data and Smart Computing (BigComp 2019), Kyoto, Japan, February 28 - March 2, 2019 (Best Paper Award).