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Jum** through local minima: Quantization in the loss landscape of vision transformers
Quantization scale and bit-width are the most important parameters when considering how
to quantize a neural network. Prior work focuses on optimizing quantization scales in a …
to quantize a neural network. Prior work focuses on optimizing quantization scales in a …
SSR: Spatial sequential hybrid architecture for latency throughput tradeoff in transformer acceleration
With the increase in the computation intensity of the chip, the mismatch between
computation layer shapes and the available computation resource significantly limits the …
computation layer shapes and the available computation resource significantly limits the …
ViTA: A vision transformer inference accelerator for edge applications
Vision Transformer models, such as ViT, Swin Transformer, and Transformer-in-Transformer,
have recently gained significant traction in computer vision tasks due to their ability to …
have recently gained significant traction in computer vision tasks due to their ability to …
Lightening-transformer: A dynamically-operated optically-interconnected photonic transformer accelerator
The wide adoption and significant computing resource cost of attention-based transformers,
eg, Vision Transformers and large language models, have driven the demand for efficient …
eg, Vision Transformers and large language models, have driven the demand for efficient …