Implementation of 128-bit Radix-4 Booth Multiplier

Rooban, S.; Nagesh, M.; Prasanna, M V S L; Sai, Govardhan

doi:10.1109/iccci50826.2021.9457004

Cited by 4 publications

(1 citation statement)

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“…Moreover, the quantity of DSPs is usually insufficient for large-scale industrial application. Hence, previous designs have attempted to reduce the multipliers for resource saving by decreasing the partial products and dots [19]. However, when implemented on FPGAs, they may increase the LUT utilization due to the inherent architectural difference.…”

Section: Problem Formulationmentioning

confidence: 99%

Resource-Efficient Optimization for FPGA-Based Convolution Accelerator

Ma,

Xu,

Song

2023

Electronics

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Convolution forms one of the most essential operations for the FPGA-based hardware accelerator. However, the existing designs often neglect the inherent architecture of FPGA, which puts forward an austere challenge on hardware resource. Even though some previous works have proposed approximate multipliers or convolution acceleration algorithms to deal with this issue, the inevitable accuracy loss and resource occupation easily lead to performance degradation. Toward this, we first propose two kinds of resource-efficient optimized accurate multipliers based on LUTs or carry chains. Then, targeting FPGA-based platforms, a generic multiply–accumulate structure is constructed by directly accumulating the partial products produced by our proposed optimized radix-4 Booth multipliers without intermediate multiplication and addition results. Experimental results demonstrate that our proposed multiplier achieves a maximum 51% look-up-table (LUT) reduction compared to the Vivado area-optimized multiplier IP. Furthermore, the convolutional process unit using the proposed structure achieves a 36% LUT reduction compared to existing methods. As case studies, the proposed method is applied to DCT transform, LeNet, and MobileNet-V3 to achieve hardware resource saving without loss of accuracy.

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Section: Problem Formulationmentioning

confidence: 99%