Passivformer

Passivformer is a term used in discussions of neural network design to denote Transformer-based models optimized for passive or energy-efficient computation. The goal is to reduce energy use, latency, and hardware demands during inference on resource-constrained devices, while preserving acceptable accuracy. Variants rely on principles such as fixed or slowly changing components, low-precision arithmetic, and sparse or event-driven processing.

Design approaches grouped under Passivformer include sparse attention patterns, kernelized attention to reduce compute, quantization and weight sharing, and architectural tweaks that limit active computations per layer. Some proposals emphasize static computation graphs to minimize dynamic work, others explore training-time techniques like knowledge distillation or pruning to retain performance after energy reductions. There is no single standard implementation.

Applications focus on mobile AI, edge devices, and real-time systems where energy and latency are critical. Evaluation uses both traditional accuracy metrics and energy-related measurements such as joules per inference, latency, and thermal behavior. The field is evolving, with ongoing debates about trade-offs and reproducibility; as of now, Passivformer design remains a collection of related ideas rather than a monolithic standard.