DATE Save the Date 17 to 19 April 2023

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W05.6.1 Invited Talk I: "Research and Design of Pass Transistor Based Multipliers and their Design for Test for Convolutional Neural Network Computation"

Start
End
Speaker
Zhiyi Yu, Sun-Yat Sen University, Zhuhai, China
Speaker
Ningyuan Yin, Sun-Yat Sen University, Zhuhai, China

Abstract: Convolutional Neural Networks (CNN) are featured with different bit widths at different layers and have been widely used in mobile and embedded applications. The implementation of a CNN may include multipliers which might consume large overheads and suffer from a high timing error rate due to their large delay. The Pass transistor logic (PTL) based multiplier is a promising solution to such issues. It uses less transistors. It also reduces the gates in the critical path and thus reduces the worst case delay. As a result, the timing error rate is reduced. In this talk, we present PTL based multipliers and the design for test (DFT). An error model is built to analyze the error rate and to help with DFT. According to the simulation results, compared to traditional CMOS based multiplier, the operation ability (measured by Joule per operation, J/OPS) of PTL multipliers could be reduced by over 20%.