Hardware-Efficient Softmax and Layer Normalization with Guaranteed Normalization for Edge Devices
In Transformer models, non-GEMM (non-General Matrix Multiplication) operations -- especially Softmax and Layer Normalization (LayerNorm) -- often dominate hardware cost due to their nonlinear nature. To address this, previous approximation studies mainly target rank-oriented tasks, which is acceptable for classification. However, edge Natural Language Processing (NLP) applications and edge generative AI are largely evaluated based on score-oriented tasks, so normalization-guaranteed non-GEMM operations are essential. We propose a hardware-efficient Softmax and LayerNorm with Guaranteed Normalization for Edge devices. Our design employs hardware-efficient approximation methods while preser...
| Authors | Dawon Choi, Hana Kim, Ji-Hoon Kim |
|---|---|
| Primary category | cs.AR |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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