Thursday 10 April 2025
Artificial Intelligence, once a realm of science fiction, has made tremendous progress in recent years. One area where AI has shown great promise is in its ability to process and understand vast amounts of data quickly and efficiently. However, as the complexity of these models increases, so too does their computational requirements, making it difficult to run them on even the most powerful computers.
Researchers have been exploring ways to speed up the processing of these complex models, and a new study published in EuroSys ’25 has made significant strides in this area. The team behind Samoyeds, an innovative acceleration system for Mixture-of-Experts (MoE) language models, has developed a novel approach that leverages Sparse Tensor Cores (SpTCs) to achieve remarkable speedups.
For those unfamiliar, MoE is a type of AI model that combines multiple expert networks to generate highly accurate predictions. These models have been shown to excel in tasks such as natural language processing and machine translation, but their computational requirements make them challenging to run on standard hardware.
The Samoyeds system tackles this problem by introducing structured sparsity into both the activations and model parameters of the MoE model. By exploiting the inherent patterns of sparsity in these components, Samoyeds is able to significantly reduce the number of calculations required to process the model, resulting in a substantial speedup.
To achieve this, the team developed a bespoke sparse data format tailored specifically for MoE computation, as well as a specialized kernel for matrix multiplication that takes advantage of SpTCs. These innovations enable Samoyeds to execute dual-side structured sparse MoE models on SpTC-equipped GPUs with remarkable efficiency.
The results are impressive: Samoyeds achieves an average speedup of 1.99× over baselines, with some experiments showing up to a 2.45× increase in performance. Moreover, the system is able to maintain high accuracy while reducing computational requirements, making it an attractive solution for those looking to deploy MoE models on resource-constrained hardware.
The potential applications of Samoyeds are vast, ranging from improving language translation capabilities to enhancing chatbots and virtual assistants. As AI continues to evolve and become increasingly complex, innovative solutions like Samoyeds will be essential in unlocking its full potential.
By leveraging the power of SpTCs and structured sparsity, the Samoyeds system has demonstrated a new way forward for accelerating MoE models, paving the way for even more sophisticated AI applications in the future.
Cite this article: “Accelerating Mixture-of-Experts Models with Structured Sparsity: A Breakthrough in Efficient Natural Language Processing”, The Science Archive, 2025.
Artificial Intelligence, Mixture-Of-Experts, Sparse Tensor Cores, Sptcs, Gpu, Machine Translation, Natural Language Processing, Acceleration System, Moe Model, Language Models
