#hardware-accelerators News & Analysis

2 articles tagged with #hardware-accelerators. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · Jun 97/10

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ScaleSweep: Accurate NVFP4 Post-Training Quantization of LLMs via Block Scale Initialization

ScaleSweep introduces an optimized block scale initialization method for NVFP4 quantization of large language models, improving upon traditional AbsMax approaches. The technique theoretically bounds the search space and empirically achieves 93% performance retention under aggressive 4-bit quantization, advancing hardware-efficient AI inference.

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AIBullisharXiv – CS AI · Feb 276/108

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GRAU: Generic Reconfigurable Activation Unit Design for Neural Network Hardware Accelerators

Researchers propose GRAU, a new reconfigurable activation unit design for neural network hardware accelerators that uses piecewise linear fitting with power-of-two slopes. The design reduces LUT consumption by over 90% compared to traditional multi-threshold activators while supporting mixed-precision quantization and nonlinear functions.