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Deep convolutional neural network (DCNN) has achieved remarkable performance on object detection and speech recognition in recent years. However, the excellent performance of a DCNN incurs high computational complexity and large memory requirement In this paper, an equal distance nonuniform quantization (ENQ) scheme and a K-means clustering nonuniform quantization (KNQ) scheme are proposed to reduce...
Convolutional Neural Network (CNN) is the state-of-the-art deep learning approach employed in various applications due to its remarkable performance. Convolutions in CNNs generally dominate the overall computation complexity and thus consume major computational power in real implementations. In this paper, efficient hardware architectures incorporating parallel fast finite impulse response (FIR) algorithm...
This paper presents a low-complexity, high-speed 4-dimensional 8-ary Phase Shift Keying Trellis Coded Modulation (4-D 8PSK TCM) decoder. In the design, an efficient architecture for the transition metrics unit (TMU) is proposed to significantly reduce the computation complexity without degrading the performance. In addition, pipelining and parallel processing techniques are exploited to increase the...
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