Neural Architecture Search for Highly Bespoke Robust Printed Neuromorphic Circuits

The market demand for next-generation flexible electronics is experiencing a significant upsurge, particularly in cost-sensitive consumer applications like smart packaging and smart bandages. These products are beyond the reach of traditional silicon-based electronics due to their high production cost and rigid form factor. Printed electronics (PE), with its adaptable and ultra-low-cost solutions, essentially meet the unique needs of these emerging application areas. This work presents a novel approach using an evolutionary algorithm (EA) to design highly bespoke printed analog neuromorphic circuits (pNCs) offering robustness against variability inherent in the printing process. By leveraging this algorithm and designing robust activation circuits, not only the resistances (weights) in the crossbar and parameters in the activation circuits, but also the types of nonlinear circuits (i.e., functional forms of activation functions) as well as the circuit topologies (neural architecture) can be learned to enhance the circuit robustness against printing variations. Experiments on 13 benchmark datasets demonstrate that, compared to the baseline, the proposed methodology can further outperform the normalized classification error rate by ≈ 55.38% and ≈ 25.11% under high-precision (±5%) and low-precision (±10%) printing scenarios, respectively. Moreover, the algorithm suggests the ReLU as the most robust activation function (AF) circuit family with only ≈ 21% susceptible to low precision (±10%) printing variation.