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Found 3 papers, 2 papers with code
Optimizing the Deployment of Tiny Transformers on Low-Power MCUs
Moreover, we show that our MHSA depth-first tiling scheme reduces the memory peak by up to 6. 19x, while the fused-weight attention can reduce the runtime by 1. 53x, and number of parameters by 25%.
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers
Transformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and audio processing.
SALSA: Simulated Annealing based Loop-Ordering Scheduler for DNN Accelerators
To meet the growing need for computational power for DNNs, multiple specialized hardware architectures have been proposed.
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