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O-POPE: High-Frequency Pipelined Outer Product based GEMM acceleration with minimal buffering overhead
arXiv:2606.02333v1 Announce Type: new Abstract: General matrix multiply (GEMM) dominates both execution time and energy consumption of modern machine learning (ML) workloads, placing increasing pressure on hardware efficiency. While quantization mitigates computational and data movement costs, accuracy-sensitive tasks such as training still require higher-precision floating-point formats. Existing floating-point GEMM accelerators face trade-offs between operating frequency, arithmetic...