
This paper introduces an analytical framework to comprehensively analyze the potential and limitations of FPGA-based spatial acceleration for efficient large language model inference. It also provides a suite of modular and reusable HLS kernels to enable high-performance FPGA-based LLM accelerators.


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optimizing-fpga-based-acceleration-for-efficient-large-language-model-inference


Optimizing FPGA-Based Acceleration for Efficient Large Language Model Inference


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This paper proposes an FPGA-based accelerator design to efficiently support the Convolution-Transformer hybrid architecture of the state-of-the-art efficient Vision Transformer, EfficientViT, by leveraging a reconfigurable architecture and a novel time-multiplexed and pipelined dataflow.


an-fpga-based-reconfigurable-accelerator-for-efficient-vision-transformer-with-convolution-transformer-hybrid-architecture


An FPGA-Based Reconfigurable Accelerator for Efficient Vision Transformer with Convolution-Transformer Hybrid Architecture
