SECDA-DSE integrates LLMs using retrieval-augmented generation and chain-of-thought prompting to automate design space exploration for FPGA-based AI accelerators, demonstrated feasible by synthesizing one valid design on a Zynq-7000 FPGA.
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Sparse FHE matrix multiplication on AMD GPUs via FIDESlib achieves 3x CPU speedup and shifts complexity from cubic to semi-linear.
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LLM-Driven Design Space Exploration of FPGA-based Accelerators
SECDA-DSE integrates LLMs using retrieval-augmented generation and chain-of-thought prompting to automate design space exploration for FPGA-based AI accelerators, demonstrated feasible by synthesizing one valid design on a Zynq-7000 FPGA.
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GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
Sparse FHE matrix multiplication on AMD GPUs via FIDESlib achieves 3x CPU speedup and shifts complexity from cubic to semi-linear.