Edge-Optimized Neuromorphic VLSI Accelerator Based On Hybrid Spiking Neural Models
DOI:
https://doi.org/10.29284/ijasis.12.1.2026.199-210Keywords:
Neuromorphic VLSI, Hybrid Spiking Neural Networks, Edge AI Accelerator, Low-Power Computing, Event-Driven Processing.Abstract
The rapid proliferation of edge intelligence has created an urgent demand for ultra-low-power, high-performance hardware accelerators capable of executing complex AI workloads under stringent energy and latency constraints. This paper presents an edge-optimized neuromorphic VLSI accelerator based on hybrid spiking neural models, specifically designed to support real-time inference in resource-constrained edge environments. The proposed architecture integrates biologically inspired spiking neurons with conventional artificial neural processing units, enabling efficient event-driven computation and adaptive learning capabilities. A hybrid processing framework is introduced, combining rate-coded and temporal spiking mechanisms to enhance representational efficiency and inference accuracy while minimizing memory access overhead. The accelerator employs parallel spike routing, on-chip synaptic weight storage, and dynamic voltage-frequency scaling to achieve optimal energy efficiency. Hardware-aware optimizations, including sparsity exploitation and spike-driven computation scheduling, significantly reduce switching activity and power consumption. Post-layout simulations using a 45-nm CMOS technology demonstrate up to 62% reduction in power consumption and 1.8× improvement in throughput compared to conventional digital CNN accelerators. Experimental evaluation on benchmark datasets confirms competitive accuracy while achieving sub-millijoule energy per inference. The proposed design establishes a scalable and energy-efficient neuromorphic VLSI framework, making it highly suitable for next-generation edge AI applications such as IoT sensing, wearable devices, and autonomous systems.
References
[1] [1] C. Mead, Neuromorphic Engineering, Proc. IEEE, vol. 78, no. 10, pp. 1629–1636, 1990.
[2] [2] M. Davies et al., “Loihi: A Neuromorphic Manycore Processor with On-Chip Learning,” IEEE Micro, vol. 38, no. 1, pp. 82–99, 2018.
[3] [3] P. A. Merolla et al., “A Million-Spiking-Neuron Integrated Circuit With a Scalable Communication Network,” Science, vol. 345, no. 6197, pp. 668–673, 2014.
[4] [4] Y. Ji et al., “Spike-Driven Transformer for Neuromorphic Vision,” NeurIPS, pp. 1–12, 2022.
[5] [5] S. Yin et al., “XNOR-RRAM: Computational RRAM Supporting Logic and In-Memory Computing,” IEEE TCAS-I, vol. 68, no. 2, pp. 633–646, 2021.
[6] [6] A. Sebastian et al., “Memory Devices and Applications for In-Memory Computing,” Nat. Nanotechnol., vol. 15, pp. 529–544, 2020.
[7] [7] W. Maass, “Networks of Spiking Neurons: The Third Generation of Neural Network Models,” Neural Netw., vol. 10, no. 9, pp. 1659–1671, 1997.
[8] Maheshwari, R.U., B.Paulchamy, Pandey, B.K. et al. Enhancing Sensing and Imaging Capabilities Through Surface Plasmon Resonance for Deepfake Image Detection. Plasmonics (2024). https://doi.org/10.1007/s11468-024-02492-1
[9] Maheshwari, Uma, and Kalpanaka Silingam. "Multimodal Image Fusion in Biometric Authentication." Fusion: Practice and Applications 1, no. 2 (2020): 7991.
[10] S. S, S. S and U. M. R, "Soft Computing based Brain Tumor Categorization with Machine Learning Techniques," 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA), Coimbatore, India, 2022, pp. 1-9, doi: 10.1109/ICACTA54488.2022.9752880.
[11] R. Uma Maheshwari, B. Paulchamy, Arun M, Vairaprakash Selvaraj, Dr. N. Naga Saranya and Dr . Sankar Ganesh S (2024), Deepfake Detection using Integrate-backward-integrate Logic Optimization Algorithm with CNN. IJEER 12(2), 696-710. DOI: 10.37391/IJEER.120248.
[12] Rajendran, U. M., & Paulchamy, J. (2021). Analysis and classification of gait characteristics. Iconic Research and Engineering Journals, 4(12).
[13] Paulchamy, B., Chidambaram, S., Jaya, J., & Maheshwari, R. U. (2021). Diagnosis of Retinal Disease Using Retinal Blood Vessel Extraction. In International Conference on Mobile Computing and Sustainable Informatics: ICMCSI 2020 (pp. 343-359). Springer International Publishing.
[14] [13] X. Peng et al., “ReRAM-Based In-Memory Computing for Neuromorphic Processors,” IEEE TCAD, vol. 40, no. 9, pp. 1706–1719, 2021.
[15] [14] Y. Zhu et al., “Bio-Inspired Adaptive Threshold Neurons for Efficient Spiking Networks,” IEEE TNNLS, pp. 1–14, 2024.
[16] [15] A. Basu et al., “Low-Power, Low-Latency Neuromorphic Computing for Edge Intelligence,” IEEE Circuits Syst. Mag., vol. 22, no. 3, pp. 6–24, 2022.
[17] [16] D. Kuzum et al., “Synaptic Electronics: Beyond Complementary Metal-Oxide Semiconductor,” Nat. Commun., vol. 13, pp. 1–15, 2022.
[18] [17] H. Kim et al., “Hybrid CMOS-RRAM Neuromorphic Processors for On-Device Learning,” IEEE JSSC, vol. 59, no. 1, pp. 77–89, 2024.
[19] [18] N. Rathi et al., “Diet-SNN: Direct Training of Deep SNNs with Hybrid Coding,” ICLR, pp. 1–14, 2023.
[20] [19] A. Costa et al., “Spiking Neural Networks for Event-Based Vision at the Edge,” IEEE TPAMI, pp. 1–16, 2024.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
