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In this work, we implement an ASIC design of the SWM-based neural network for the image recognition task, and it is tested with the MNIST dataset. We evaluate the structure matrices based compression technique using TIMIT benchmark, the most commonly used dataset for automatic speech recognition (ASR) application. POSTSUBSCRIPT are diagonal matrices for peephole connections, which can be considered as vectors during matrix-vector multiplication. However, this can cause instability of the performance so we should check it carefully with power pulsing control. Since the power consumption of SWM-based LSTM is only half of the ESE, the energy efficiency gain is higher than performance. The most distinct feature of the SKIROC2 chip is the power pulsing functionality, which partially switches off power when not needed, e. We report a performance study with the evaluation boards, including signal-to-noise ratio and TDC performance with comparing SKIROC2 and an updated version, SKIROC2A. 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