SpikeSynth: Energy-Efficient Adaptive Analog Printed Spiking Neural Networks

Biologically-inspired Spiking Neural Networks (SNNs) have emerged as a promising avenue toward energy-efficient neuromorphic computing, particularly in edge applications such as soft robotics, wearable health monitors, and IoT devices. Printed Electronics (PE), offering advantages of ultra-low cost fabrication and mechanical flexibility, present a viable platform to realize such neuromorphic systems at scale. However, designing adaptable and efficient spiking circuits
that meet the unique constraints of PE applications remains a challenge. To address this, we propose a novel analog spiking neuromorphic circuit with a learnable spike generator (LSG). Unlike fixed-threshold models, our generator adapts spike timing dynamics during training, enabling better task-specific performance. To optimize for ultra-low power consumption on resource-constrained platforms, we further introduce a robustness-aware training framework that further minimizes the energy consumption adaptively. Simulation results across 13 benchmarks demonstrate an average 57.6% power reduction for
the LSG while improving the average classification accuracy by 8%, area and energy reduction by 89% and 28.7% respectively compared to the state-of-the-art printed analog spiking neural networks (P-SNNs).