EFLOP: A Sparsity-Aware Metric for Evaluating Computational Cost in Spiking and Non-Spiking Neural Networks

Abstract Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both ANNs and SNNs have been proposed as candidates for these tasks and in particular SNNs have been proposed as energy-efficient because they leverage temporal sparsity in the outputs. However, existing computational frameworks fail to accurately estimate the cost of running sparse networks on modern time-stepped hardware, which exploits sparsity by skipping zero-valued operations. Meanwhile, weight sparsity-aware training remains underexplored for SNN and lacks systematic benchmarking against optimized ANN, making fair comparisons between the two paradigms difficult. To bridge this gap, we introduce EFLOP, a metric that accounts for the sparse operations during pre-activation updates of both ANN and SNN. Applying weight sparsity-aware training to both SNN and ANN, we achieve up to 8.9x reduction in EFLOP for GRU models and 3.6x for LIF models by sparsifying weights by 80%, without sacrificing accuracy on the SHD and SSC datasets. Our findings highlight the critical role of network sparsity in designing energy-efficient neural networks and establish EFLOP as a robust framework for cross-paradigm comparisons.