A Comparative Study on Hardware Performance Between Sparse and Dense Neural Networks

This research project was published by MahdiKhoshnevis on GitHub (original title: sparse_dense_comparison, link: https://github.com/MahdiKhoshnevis/sparse_dense_comparison, release date: June 9, 2026). Its core objective is to systematically compare the performance of sparse and dense neural networks across different hardware platforms and explore the advantages and limitations of model sparsification in practical deployment.

Modern deep learning models (such as GPT-4 and PaLM) are growing rapidly in scale, bringing challenges in computation, storage, and energy consumption. Neural network sparsification theoretically improves storage efficiency (via compressed format storage), accelerates computation (by skipping zero values), and reduces energy consumption (by decreasing memory access and computation). However, actual gains depend on hardware support and optimization, which is the focus of this study.

Sparsification methods are divided into structured (taking filters/channels/layers as units, easy to implement but with more capacity loss) and unstructured (pruning individual weights, retaining more capacity but with irregular access). The training process includes dense pre-training → importance evaluation → pruning → sparse fine-tuning → iterative optimization. Storage formats include CSR/CSC (high sparsity), COO (coordinate storage), and block sparse (balancing efficiency and regularity).

CPU: General-purpose CPUs have limited support, and SIMD struggles to utilize cache. GPU: cuSPARSE is optimized, but sparse convolution is limited by thread branching and memory coalescing. Dedicated AI accelerators: NVIDIA Ampere (2:4 structured sparsity, 2x theoretical speedup), Intel Habana Gaudi (optimized for deep learning), Graphcore IPU (parallelism suitable for sparse graphs), mobile NPUs (e.g., Apple NE, Qualcomm Hexagon, optimized for battery life).

Model selection: ResNet, MobileNet, Transformer, lightweight networks. Sparsity configuration: 50%, 70%, 90%. Hardware coverage: server GPUs (A100, RTX), consumer GPUs, CPU, edge devices (Jetson, Coral). Evaluation metrics: accuracy, inference latency, throughput, energy consumption, memory usage.

The benefits of sparsification are conditional (depending on sparse patterns, hardware/software optimization, sparsity level, and workload). Structured sparsity is more practical (good acceleration effect on general hardware). Edge devices benefit more significantly (high value in resource-constrained scenarios). Hardware-software co-design is required (combining algorithms with hardware/software optimization).

Guides model design (whether to use sparse architecture in specific scenarios), hardware selection (platforms suitable for sparse models), optimization directions (identifying bottlenecks), and standardized benchmarks (promoting comparability). In the future, with the advancement of sparse training technologies (such as RigL, SR-STEP) and hardware support, sparse networks are expected to be widely deployed, and this study provides an empirical basis.