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Found 5 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.
Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition
Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms.
CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency
We present a 3. 1 POp/s/W fully digital hardware accelerator for ternary neural networks.
Hardware Architecture
TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars
Furthermore, the gesture recognition classifier has been implemented on a Parallel Ultra-Low Power Processor, demonstrating that real-time prediction is feasible with only 21 mW of power consumption for the full TCN sequence prediction network, while a system-level power consumption of less than 100 mW is achieved.
