Sparse Computations in Deep Learning Inference

2025-12-02

AI Summary

The computational and energy overhead of deep learning inference is increasingly prohibitive, yet sparsity—a key optimization avenue—remains underutilized in production systems. Method: Targeting performance engineers, this work systematically surveys structured and unstructured sparsity exploitable in DNN inference and proposes an end-to-end engineering methodology—from sparse model representation to efficient sparse kernels (SpMM/SDDMM). We implement and benchmark multiple sparse computation schemes on CPU and GPU platforms, integrating support for mainstream frameworks, toolchains, and datasets. Contribution/Results: We present the first production-grade sparse inference reference framework encompassing hardware adaptation, kernel optimization, and deployment validation. Experiments demonstrate 2–5× inference speedup and substantial energy efficiency gains across representative models, establishing a reproducible, scalable practical paradigm for industrial deployment of sparse deep learning.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The computational demands of modern Deep Neural Networks (DNNs) are immense and constantly growing. While training costs usually capture public attention, inference demands are also contributing in significant computational, energy and environmental footprints. Sparsity stands out as a critical mechanism for drastically reducing these resource demands. However, its potential remains largely untapped and is not yet fully incorporated in production AI systems. To bridge this gap, this work provides the necessary knowledge and insights for performance engineers keen to get involved in deep learning inference optimization. In particular, in this work we: a) discuss the various forms of sparsity that can be utilized in DNN inference, b) explain how the original dense computations translate to sparse kernels, c) provide an extensive bibliographic review of the state-of-the-art in the implementation of these kernels for CPUs and GPUs, d) discuss the availability of sparse datasets in support of sparsity-related research and development, e) explore the current software tools and frameworks that provide robust sparsity support, and f) present evaluation results of different implementations of the key SpMM and SDDMM kernels on CPU and GPU platforms. Ultimately, this paper aims to serve as a resource for performance engineers seeking to develop and deploy highly efficient sparse deep learning models in productions.

Problem

• Optimizing sparse computations for efficient deep learning inference
• Bridging the gap between sparsity potential and production AI systems
• Providing knowledge for performance engineers on sparse DNN implementation

Innovation

• Utilizing sparsity to reduce DNN inference resource demands
• Translating dense computations into efficient sparse kernels
• Evaluating sparse kernel implementations on CPU and GPU platforms