Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!

TL;DR

This paper introduces SMC-Bench, a comprehensive benchmark with diverse tasks to evaluate sparse neural network algorithms, revealing their limitations and encouraging more scalable, generalizable solutions.

Contribution

The paper presents SMC-Bench, a new benchmark with diverse tasks for evaluating sparse neural networks, exposing limitations of current algorithms and promoting better development.

Findings

State-of-the-art sparse algorithms often fail on SMC-Bench tasks.

Sparse algorithms perform poorly even at low sparsity levels like 5%.

The benchmark encourages development of more scalable and generalizable sparse methods.

Abstract

Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large-scale models. Similar to their dense counterparts, recent SNNs generalize just as well and are equipped with numerous favorable benefits (e.g., low complexity, high scalability, and robustness), sometimes even better than the original dense networks. As research effort is focused on developing increasingly sophisticated sparse algorithms, it is startling that a comprehensive benchmark to evaluate the effectiveness of these algorithms has been highly overlooked. In absence of a carefully crafted evaluation benchmark, most if not all, sparse algorithms are evaluated against fairly simple and naive tasks (eg. CIFAR, ImageNet, GLUE, etc.), which can potentially camouflage many advantages as well unexpected predicaments of SNNs. In pursuit of a more general evaluation and unveiling the true potential of sparse algorithms, we introduce "Sparsity May Cry" Benchmark (SMC-Bench), a collection of carefully-curated 4 diverse tasks with 10 datasets, that accounts for capturing a wide range of domain-specific and sophisticated knowledge. Our systemic evaluation of the most representative sparse algorithms reveals an important obscured observation: the state-of-the-art magnitude- and/or gradient-based sparse algorithms seemingly fail to perform on SMC-Bench when applied out-of-the-box, sometimes at significantly trivial sparsity as low as 5%. By incorporating these well-thought and diverse tasks, SMC-Bench is designed to favor and encourage the development of more scalable and generalizable sparse algorithms.