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Deep neural networks (DNNs) have emerged as the most powerful machine learning technique in numerous artificial intelligent applications. However, the large sizes of DNNs make themselves both computation and memory intensive, thereby limiting the hardware performance of dedicated DNN accelerators. In this paper, we propose a holistic framework for energy-efficient high-performance highly-compressed...
Low-power brain-inspired hardware systems have gained significant traction in recent years. They offer high energy efficiency and massive parallelism due to the distributed and asynchronous nature of neural computation through low-energy spikes. One such platform is the IBM TrueNorth Neurosynaptic System. Recently TrueNorth compatible representation learning algorithms have emerged, achieving close...
Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloudcomputing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become...
Spike-Timing Dependent Plasticity (STDP), the canonical learning rule for spiking neural networks (SNN), is gaining tremendous interest because of its simplicity, efficiency and biological plausibility. However, to date, multilayer feed-forward networks of spiking neurons are either only partially trained using STDP or pre-trained using traditional deep neural networks which are converted to deep...
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),...
Deep Convolutional Neural Networks (DCNNs) have been demonstrated as effective models for understanding image content. The computation behind DCNNs highly relies on the capability of hardware resources due to the deep structure. DCNNs have been implemented on different large-scale computing platforms. However, there is a trend that DCNNs have been embedded into light-weight local systems, which requires...
Real-time anomaly detection for streaming data is a desirable feature for mobile devices or unmanned systems. The key challenge is how to deliver required performance under the stringent power constraint. To address the paradox between performance and power consumption, brain-inspired hardware, such as the IBM Neurosynaptic System, has been developed to enable low power implementation of large-scale...
In recent years, Deep Convolutional Neural Network (DCNN) has become the dominant approach for almost all recognition and detection tasks and outperformed humans on certain tasks. Nevertheless, the high power consumptions and complex topologies have hindered the widespread deployment of DCNNs, particularly in wearable devices and embedded systems with limited area and power budget. This paper presents...
Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. This problem has been tackled by many previous researchers using techniques such as deep learning networks and hidden Markov Models (HMM), etc. In this work we aim at offline fuzzy recognition of handwritten texts. A probabilistic inference network that performs...
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...
With the proliferation of application specific accelerators, the use of heterogeneous clusters is rapidly increasing. Consisting of processors with different architectures, a heterogeneous cluster aims at providing different performance and cost tradeoffs for different types of workloads. In order to achieve peak performance, software running on heterogeneous cluster needs to be designed carefully...
The superb efficiency and noise resilience of human cognizance comes from the extensive highly associative memory. For example, it is easy for human to recognize occluded or incomplete text images based on its context. Associative inference in the neocortex system is a concurrent process. Serial implementation of this concurrent process not only hinders its performance, but also limits the quality...
Linear programming is required in a wide variety of application including routing, scheduling, and various optimization problems. The primal-dual interior point (PDIP) method is state-of-the-art algorithm for solving linear programs, and can be decomposed to matrix-vector multiplication and solving systems of linear equations, both of which can be conducted by the emerging memristor crossbar technique...
The problem of bridging the gap between image and natural language has gained more and more attention in recent years. This paper continues to push the study and improves the bidirectional retrieval performance across the modalities. Unlike previous works that target at single sentence densely describing the image objects, we extend the focus to associating deep image representations with noisy texts...
Spiking neural networks are rapidly gaining popularity for their ability to perform efficient computation akin to the way a brain processes information. It has the potential to achieve low cost and high energy efficiency due to the distributed nature of neural computation and the use of low energy spikes for information exchange. A stochastic spiking neural network naturally can be used to realize...
The ability of neural networks to perform pattern recognition, classification and associative memory, is essential to applications such as image and speech recognition, natural language understanding, decision making etc. In spiking neural networks (SNNs), information is encoded as sparsely distributed train of spikes, which allows learning through the spike-timing dependent plasticity (STDP) property...
The emerging field of neuromorphic computing is offering a possible pathway for approaching the brain's computing performance and energy efficiency for cognitive applications such as pattern recognition, speech understanding, natural language processing etc. In spiking neural networks (SNNs), information is encoded as sparsely distributed spike trains, enabling learning through the spike-timing dependent...
High utilization of hardware resources is the key for designing performance and power optimized GPUapplications. The efficiency of applications and kernels, which do not fully utilize the GPU resources, can be improved through concurrent execution with independent kernels and/or applications. Hyper-Q enables multiple CPU threads or processes to launch work on a single GPU simultaneously for increased...
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