  Spiking Neural Networks (SNNs) have emerged as a promising alternative to
traditional Deep Neural Networks for low-power computing. However, the
effectiveness of SNNs is not solely determined by their performance but also by
their energy consumption, prediction speed, and robustness to noise. The recent
method Fast \& Deep, along with others, achieves fast and energy-efficient
computation by constraining neurons to fire at most once. Known as
Time-To-First-Spike (TTFS), this constraint however restricts the capabilities
of SNNs in many aspects. In this work, we explore the relationships between
performance, energy consumption, speed and stability when using this
constraint. More precisely, we highlight the existence of tradeoffs where
performance and robustness are gained at the cost of sparsity and prediction
latency. To improve these tradeoffs, we propose a relaxed version of Fast \&
Deep that allows for multiple spikes per neuron. Our experiments show that
relaxing the spike constraint provides higher performance while also benefiting
from faster convergence, similar sparsity, comparable prediction latency, and
better robustness to noise compared to TTFS SNNs. By highlighting the
limitations of TTFS and demonstrating the advantages of unconstrained SNNs we
provide valuable insight for the development of effective learning strategies
for neuromorphic computing.
