aiqbench analysis
This paper introduces GRAU, a novel hardware unit designed to optimize quantized neural network accelerators by fusing activation and requantization functions with minimal logic overhead. By utilizing piecewise-linear fitting, the architecture significantly reduces lookup table consumption while supporting modern nonlinear activations like SiLU and GELU for improved efficiency at the edge. The work addresses critical scalability challenges in AI hardware through a practical trade-off analysis between approximation accuracy and circuit cost.
GRAU: A Generic Runtime-Reconfigurable Activation-Requantization Unit for Quantized Neural Network Accelerators
arXiv:2602.22352v3 Announce Type: replace-cross Abstract: As neural networks continue to scale, low-precision quantization is widely adopted in edge accelerators, but conventional multi-threshold activation hardware requires 2^n-1 thresholds for n-bit integer outputs, leading to rapidly increasing hardware costs. We propose GRAU, a Generic Runtime-Reconfigurable Activation-Requantization Unit that fuses batch normalization, nonlinear activation, and requantization through piecewise-linear fitting with Power-of-Two slopes. GRAU requires only basic comparators and 1-bit right shifters, while supporting mixed-precision quantization and runtime switching among post-accumulation functions, including modern nonlinear activations such as SiLU and GELU. Compared with multi-threshold activation-requantization units, GRAU reduces LUT consumption by approximately 90% or more while improving hardware efficiency, flexibility, and scalability. We further explore the trade-off between accuracy and hardware cost across different approximation settings and provide an open-source toolchain for GRAU-aware network training.
Abstract:As neural networks continue to scale, low-precision quantization is widely adopted in edge accelerators, but conventional multi-threshold activation hardware requires 2^n-1 thresholds for n-bit integer outputs, leading to rapidly increasing hardware costs. We propose GRAU, a Generic Runtime-Reconfigurable Activation-Requantization Unit that fuses batch normalization, nonlinear activation, and requantization through piecewise-linear fitting with Power-of-Two slopes. GRAU requires only basic comparators and 1-bit right shifters, while supporting mixed-precision quantization and runtime switching among post-accumulation functions, including modern nonlinear activations such as SiLU and GELU. Compared with multi-threshold activation-requantization units, GRAU reduces LUT consumption by approximately 90% or more while improving hardware efficiency, flexibility, and scalability. We further explore the trade-off between accuracy and hardware cost across different approximation settings and provide an open-source toolchain for GRAU-aware network training.
Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
arXiv:2602.22352 [cs.AR]
(or arXiv:2602.22352v3 [cs.AR] for this version)
https://doi.org/10.48550/arXiv.2602.22352 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 221 words