  Spiking neural networks (SNNs) process time-series data via internal
event-driven neural dynamics. The energy consumption of an SNN depends on the
number of spikes exchanged between neurons over the course of the input
presentation. Typically, decisions are produced after the entire input sequence
has been processed. This results in latency and energy consumption levels that
are fairly uniform across inputs. However, as explored in recent work, SNNs can
produce an early decision when the SNN model is sufficiently ``confident'',
adapting delay and energy consumption to the difficulty of each example.
Existing techniques are based on heuristic measures of confidence that do not
provide reliability guarantees, potentially exiting too early. In this paper,
we introduce a novel delay-adaptive SNN-based inference methodology that,
wrapping around any pre-trained SNN classifier, provides guaranteed reliability
for the decisions produced at input-dependent stopping times. The approach,
dubbed SpikeCP, leverages tools from conformal prediction (CP). It entails
minimal complexity increase as compared to the underlying SNN, requiring only
additional thresholding and counting operations at run time. SpikeCP is also
extended to integrate a CP-aware training phase that targets delay performance.
Variants of CP based on alternative confidence correction schemes, from
Bonferroni to Simes, are explored, and extensive experiments are described
using the MNIST-DVS data set, DVS128 Gesture dataset, and CIFAR-10 dataset.
