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You don't have to be a culinary genius to see just how dirty pots and pans get after just a couple of uses - especially the bottoms. Although different network structures target at specific applications, they have the similarity in construction principle, i.e., multiple layers connected in series for feature extraction (Lee et al., 2009; Karpathy et al., 2014). DNNs are commonly made up of three-layer types: Fully-connected (FC) and convolutional layers (CONV), and pooling layers (POOL). POSTSUBSCRIPT. The data are generated either by a noise model, consisting of a standard normal distribution (i.e., zero mean and unit variance), or a signal mode, consisting of a normal distribution with some non-zero mean and unit variance<br><br>The baseline system (IBM TrueNorth) has two different DNNs of MNIST datasets at two accuracy levels. The third DNN of MNIST dataset has a CNN structure similar to LeNet-5 (LeCun et al., 1995), which achieves 99.0% accuracy. 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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