Hardware-Efficient Logarithmic Floating-Point Multipliers for Error-Tolerant Applications
Hardware-Efficient Logarithmic Floating-Point
Multipliers for Error-Tolerant Applications
Abstract:
The increasing computational intensity of important new applications poses a challenge for their use in resourcerestricted devices. Approximate computing using power-efficient arithmetic circuits is one of the emerging strategies to reach this objective. In this article, five hardware-efficient logarithmic floating-point (FP) multipliers are proposed, which all use simple operators, such as adders and multiplexers, to replace complex and more costly conventional FP multipliers. Radix-4 logarithms are used to further reduce the hardware complexity. These designs produce double-sided error distributions to mitigate error accumulation in complex computations. The proposed multipliers provide superior trade-offs between accuracy and hardware, with up to 30.8% higher accuracy than a recent logarithmic FP design or up to 68× less energy than the conventional FP multiplier. Using the proposed FP logarithmic multipliers in JPEG image compression achieves higher image quality than a recent logarithmic multiplier design with up to 4.7 dB larger peak signal-to-noise ratio. For training in benchmark NN applications, the proposed FP multipliers can slightly improve the classification accuracy while achieving 4.2× less energy and 2.2× smaller area than the state-of-the-art design.
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Hardware-Efficient Logarithmic Floating-Point
Multipliers for Error-Tolerant Applications
