Floating-Point Multiply-Add with Approximate Normalization for Low-Cost Matrix Engines
<|reference_start|>Floating-Point Multiply-Add with Approximate Normalization for Low-Cost Matrix Engines: The widespread adoption of machine learning algorithms necessitates hardware acceleration to ensure efficient performance. This acceleration relies on custom matrix engines that operate on full or reduced-precision floating-point arithmetic. However, conventional floating-point implementations can be power hungry. This paper proposes a method to improve the energy efficiency of the matrix engines used in machine learning algorithm acceleration. Our approach leverages approximate normalization within the floating-point multiply-add units as a means to reduce their hardware complexity, without sacrificing overall machine-learning model accuracy. Hardware synthesis results show that this technique reduces area and power consumption roughly by 16% and 13% on average for Bfloat16 format. Also, the error introduced in transformer model accuracy is 1% on average, for the most efficient configuration of the proposed approach.<|reference_end|>
@article{alexandridis2024floating-point,
    title={Floating-Point Multiply-Add with Approximate Normalization for Low-Cost
  Matrix Engines},
    author={Kosmas Alexandridis, Christodoulos Peltekis, Dionysios Filippas,
  Giorgos Dimitrakopoulos},
    journal={arXiv preprint arXiv:2408.11997},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11997},
    primaryClass={cs.AR}
}