AIBullisharXiv – CS AI · Feb 277/106
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Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Accelerators
Researchers developed a runtime-reconfigurable bitwise systolic array architecture for multi-precision quantized neural networks on FPGA hardware accelerators. The system achieves 1.3-3.6x speedup on mixed-precision models while supporting higher clock frequencies up to 250MHz, addressing the trade-off between hardware efficiency and inference accuracy.