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Transformer models have achieved remarkable success in various machinelearning tasks but suffer from high computational complexity and resourcerequirements. The quadratic complexity of the self-attention mechanism furtherexacerbates these challenges when dealing with long sequences and largedatasets. Specialized AI hardware accelerators, such as the Habana GAUDIarchitecture, offer a promising solution to tackle these issues. GAUDI featuresa Matrix Multiplication Engine (MME) and a cluster of fully programmable TensorProcessing Cores (TPC). This paper explores the untapped potential of usingGAUDI processors to accelerate Transformer-based models, addressing keychallenges in the process. Firstly, we provide a comprehensive performancecomparison between the MME and TPC components, illuminating their relativestrengths and weaknesses. Secondly, we explore strategies to optimize MME andTPC utilization, offering practical insights to enhance computationalefficiency. Thirdly, we evaluate the performance of Transformers on GAUDI,particularly in handling long sequences and uncovering performance bottlenecks.Lastly, we evaluate the end-to-end performance of two Transformer-based largelanguage models (LLM) on GAUDI. The contributions of this work encompasspractical insights for practitioners and researchers alike. We delve intoGAUDI's capabilities for Transformers through systematic profiling, analysis,and optimization exploration. Our study bridges a research gap and offers aroadmap for optimizing Transformer-based model training on the GAUDIarchitecture.

Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors