Tiny Transformer

Tiny Transformers are significantly smaller versions of standard Transformer models, aiming to achieve comparable performance with drastically reduced computational cost and memory footprint, making them suitable for deployment on resource-constrained devices like microcontrollers and edge computing platforms. Research focuses on efficient model compression techniques like pruning and knowledge distillation, exploring novel architectures and training methodologies to mitigate the performance loss often associated with model miniaturization. This work is crucial for expanding the applicability of Transformer-based solutions to a wider range of applications and devices, particularly in areas like wearable health monitoring, mobile robotics, and low-power embedded systems.