Abstract-Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in various tasks. There is a timely need to map the latest software DCNNs to application-specific hardware, in order to achieve orders of magnitude improvement in performance, energy efficiency and compactness. Stochastic Computing (SC), as a low-cost alternative to the conventional binary computing paradigm, has the potential to enable massively parallel and highly scalable hardware implementation of DCNNs. One major challenge in SC based DCNNs is designing accurate nonlinear activation functions, which have a significant impact on the network-level accuracy but cannot be implemented accurately by existing SC computing blocks. In this paper, we design and optimize SC based neurons, and we propose highly accurate activation designs for the three most frequently used activation functions in software DCNNs, i.e, hyperbolic tangent, logistic, and rectified linear units. Experimental results on LeNet-5 using MNIST dataset demonstrate that compared with a binary ASIC hardware DCNN, the DCNN with the proposed SC neurons can achieve up to 61X, 151X, and 2X improvement in terms of area, power, and energy, respectively, at the cost of small precision degradation. In addition, the SC approach achieves up to 21X and 41X of the area, 41X and 72X of the power, and 198200X and 96443X of the energy, compared with CPU and GPU approaches, respectively, while the error is increased by less than 3.07%. ReLU activation is suggested for future SC based DCNNs considering its superior performance under a small bit stream length.

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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 is taking advantage of the fantastic features of stochastic computing methods. Stochastic computing is a type of data representation and processing technique, which uses a binary bit stream to represent a probability number (by counting the number of ones in this bit stream). In the stochastic computing area, some key arithmetic operations such as additions or multiplications can be implemented with very simple components like AND gates or multiplexers, respectively. Thus it provides an immense design space for integrating a large amount of neurons and enabling fully parallel and scalable hardware implementations of largescale deep learning systems. In this paper, we present a reconfigurable large-scale deep learning system based on stochastic computing technologies, including the design of the neuron, the convolution function, the back-propagation function and some other basic operations. And the network-on-chip technique is also proposed in this paper to achieve the goal of implementing a large-scale hardware system. Our experiments validate the functionality of reconfigurable deep learning systems using stochastic computing, and demonstrate that when the bit streams are set to be 8192 bits, classification of MNIST digits by stochastic computing can perform as low error rate as that by normal arithmetic operations.

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Abstract-The recent breakthroughs of deep reinforcement learning (DRL) technique in Alpha Go and playing Atari have set a good example in handling large state and actions spaces of complicated control problems. The DRL technique is comprised of (i) an offline deep neural network (DNN) construction phase, which derives the correlation between each state-action pair of the system and its value function, and (ii) an online deep Qlearning phase, which adaptively derives the optimal action and updates value estimates.In this paper, we first present the general DRL framework, which can be widely utilized in many applications with different optimization objectives. This is followed by the introduction of three specific applications: the cloud computing resource allocation problem, the residential smart grid task scheduling problem, and building HVAC system optimal control problem. The effectiveness of the DRL technique in these three cyber-physical applications have been validated. Finally, this paper investigates the stochastic computing-based hardware implementations of the DRL framework, which consumes a significant improvement in area efficiency and power consumption compared with binarybased implementation counterparts.