<i>ApproxiMorph</i> : Energy-Efficient Neuromorphic System With Layer-Wise Approximation of Spiking Neural Networks and 3-D-Stacked SRAM

This paper proposes ApproxiMorph, a comprehensive framework for both software and hardware co-design, targeting energy-efficient AI applications using 3D-IC-based neuromorphic systems. By leveraging parallel interconnections and high-bandwidth communications inherent to 3D-ICs, and the noise-resilience characteristics of spiking neural networks (SNNs), ApproxiMorph achieves significant power savings by exploiting (1) approximate implementation of neuron cells, (2) layer-wise approximation of SNNs through the heuristic exploration algorithm, (3) reduced-voltage operation in the 3D-stacked SRAM, and (4) incorporating a weight-tuning method. As a result, to search for the energy-optimal layer-wise approximation, ApproxiMorph explores only 0.44—0.67% of all possible combinations, achieving a 28.06% power saving for additions with a 0.60% accuracy loss in comparison to the baseline SNN for MNIST. In the VGG16 for CIFAR-10, ApproxiMorph searches around 103 combinations from over 1017 possible solutions, resulting in a 29.16% power saving with slight accuracy gain. Furthermore, integrating all methods enhances the accuracy of approximate implementations and demonstrates higher error resilience than accurate implementations.