Early Exit is an adaptive inference optimization technique for deep neural networks where computation terminates at an intermediate layer when a confidence criterion is met, rather than propagating through all layers. Each potential exit point includes a lightweight classifier head that evaluates whether the current representation is sufficiently confident for the final prediction, enabling easier inputs to be processed with fewer layers and lower latency.

Why Early Exit Matters in AI/ML: Early exit provides input-adaptive computation that reduces average inference latency and energy consumption by allocating fewer computational resources to simpler inputs while preserving full model capacity for difficult examples.

• Confidence-based termination — At each exit point, a classifier head produces a prediction and confidence score (e.g., max softmax probability, entropy); if confidence exceeds a threshold, computation stops and the intermediate prediction is returned • Dynamic depth — Different inputs traverse different numbers of layers: simple, unambiguous inputs may exit after 2-3 layers while complex, ambiguous inputs use the full network depth, optimizing average compute per input • Exit ramp design — Exit classifiers are typically lightweight (linear layer + softmax) attached every N layers (e.g., every 3 layers in a 12-layer BERT); they must be accurate yet cheap to avoid overhead exceeding savings • Training strategies — Joint training with weighted losses at each exit point (early exits weighted lower) ensures all exits produce valid predictions; alternatively, self-distillation from the final layer teaches early exits to approximate full-model behavior • Latency-quality tradeoff — Adjusting the confidence threshold controls the exit distribution: lower thresholds exit earlier (faster, slightly less accurate) while higher thresholds push more inputs to deeper layers (slower, more accurate)

Early exit is a powerful inference optimization that provides input-adaptive computation depth, enabling transformer and deep network models to process simple inputs with a fraction of the full model's computational cost while maintaining high accuracy through confidence-calibrated dynamic termination at intermediate layers.