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Deep neural network (DNN) has emerged as a powerful machine learning technique for various artificial intelligence applications. Due to the unique advantages on speed, area, and power, specific hardware design has become a very attractive solution for the efficient deployment of DNN. However, the huge resource cost of multipliers makes the fully-parallel implementations of multiplication-intensive...
Neural networks are becoming prevalent in many areas, such as pattern recognition and medical diagnosis. Stochastic computing is one potential solution for neural networks implemented in low-power back-end devices such as solar-powered devices and Internet-of-things (IoT) devices. In this paper, we investigate a new architecture of stochastic neural networks with a hardware-oriented approximate activation...
K-nearest neighbors (KNN) algorithm is a powerful classification approach in various machine learning and pattern recognition tasks. As a non-parametric algorithm, KNN is viewed as one of the most efficient machine learning algorithms and hence it is widely adopted in various practical applications. However, due to its extensive use of high-complexity multiplication, to date the efficient hardware...
Deep Convolutional Neural Networks (DCNN), a branch of Deep Neural Networks which use the deep graph with multiple processing layers, enables the convolutional model to finely abstract the high-level features behind an image. Large-scale applications using DCNN mainly operate in high-performance server clusters, GPUs or FPGA clusters; it is restricted to extend the applications onto mobile/wearable...
Deep Learning, as an important branch of machine learning and neural network, is playing an increasingly important role in a number of fields like computer vision, natural language processing, etc. However, large-scale deep learning systems mainly operate in high-performance server clusters, thus restricting the application extensions to personal or mobile devices. The solution proposed in this paper...
Finite impulse response (FIR) filter is the basic functional component in various signal processing and communication systems. In many practical applications that have stringent requirement on spectrum, long FIR filters are needed to achieve the desired filtering performance. However, because a T-tap FIR filter requires T copies of high-complexity multiplier, the conventional design of long FIR filter...
Polar codes have become one of the most attractive topics in coding theory community because of their provable capacity-achieving property. Belief propagation (BP) algorithm, as one o f the popular approaches for decoding polar codes, has unique advantage of high parallelism but suffers from high computation complexity, which translates to very large silicon area and high power consumption. This paper,...
In recent years stochastic computing (SC) is re-gaining increasing attention for its unique advantages on low hardware cost and strong error resilience that are the key metrics for nanoscale CMOS era. However, the potential deployment of SC in practical applications is impeded by the long latency of sequential bit-stream and large complexity of pseudo random number generator (PRNG). Aiming to mitigate...
Discrete Fourier Transformation (DFT)/Fast Fourier Transformation (FFT) are the widely used techniques in numerous modern signal processing applications. In general, because of their inherent multiplication-intensive characteristics, the hardware implementations of DFT/FFT usually require a large amount of hardware resource, which limits their applications in area-constraint scenarios. To overcome...
Polar codes have emerged as the most favorable channel codes for their unique capacity-achieving property. To date, numerous approaches for efficient decoding of polar codes have been reported. However, these prior efforts focused on design of polar decoders via deterministic computation, while the behavior of stochastic polar decoder, which can have potential advantages such as low complexity and...
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