--The ever-increasing demand from mobile Machine Learning (ML) applications calls for evermore powerful on-chip computing resources. Mobile devices are empowered with Heterogeneous Multi-Processor Systems on Chips (HMPSoCs) to process ML workloads such as Convolutional Neural Network (CNN) inference. These different components are capable of independently performing inference but with very different power-performance characteristics. In this article, we provide a quantitative evaluation of the inference capabilities of the different components on HMPSoCs. Finally, we explore the performance limit of the HMPSoCs by synergistically engaging all the components concurrently. The tremendous popularity of neural-network (NN) based machine learning applications in recent years has been fuelled partly by the increased capability of the compute engines, in particular, the GPUs. Traditionally, both the network training and inference were performed on the cloud with mobile devices only acting as user interfaces. However, enriched user experience now demands inference to be performed on the mobile devices themselves with high accuracy and throughput. In this article, we look at NN-enabled vision applications on mobile devices. These applications extract high-level semantic information from real-time video streams and predominately use Convolutional Neural Networks (CNNs).

The wait is finally over. Huawei debuts the world's most powerful AI processor – meet the Ascend 910. After a year of on-going testing and development, it's a fact that the Ascend 910 processor has these advantageous features: high computing power, high level of integration and ultra-fast interconnection. With these high-performance benefits, the Ascend 910 is incredibly low in power consumption. The details are in our video.

--Due to the drastic increase of mobile traffic, wireless caching is proposed to serve repeated requests for content download. T o determine the caching scheme for decentralized caching networks, the content preference learning problem based on mobility prediction is studied. We first formulate preference prediction as a decentralized regularized multi-task learning (DRMTL) problem without considering the mobility of mobile terminals (MTs). The problem is solved by a hybrid Jacobian and Gauss-Seidel proximal multi-block alternating direction method (ADMM) based algorithm, which is proven to conditionally converge to the optimal solution with a rate O (1 / k) . Then we use the tool of Markov renewal process to predict the moving path and sojourn time for MTs, and integrate the mobility pattern with the DRMTL model by reweighting the training samples and introducing a transfer penalty in the objective. We solve the problem and prove that the developed algorithm has the same convergence property but with different conditions. Through simulation we show the convergence analysis on proposed algorithms. Our real trace driven experiments illustrate that the mobility-aware DRMTL model can provide a more accurate prediction on geography preference than DRMTL model. Besides, the hit ratio achieved by most popular proactive caching (MPC) policy with preference predicted by mobility-aware DRMTL outperforms the MPC with preference from DRMTL and random caching (RC) schemes. As a promising technology for the fifth-generation (5G) wireless networks and beyond, proactive caching can alleviate the heavy traffic burden on backhaul links and reduce service delay, through proactively storing popular contents at base stations (BSs) and mobile terminals (MTs) [1]-[3]. With the limitation of storage memory, determining where and what to cache in content centric wireless networks becomes one of the main challenges in the design of proactive caching schemes. Among the various factors affecting the wireless caching design, involving the mobility of MTs and learning content preference are two critical challenges, which have attracted more and more research interest recently. A. background Current investigation on mobility aware wireless caching mainly includes two aspects: studying the impact of MT mobility on caching schemes [4]-[7], and optimizing the wireless caching schemes based on the mobility information of MTs Y u Y e, Ming Xiao and Mikael Skoglund are with the School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH), Stockholm, Sweden (email: yu9@kth.se,

The United States Geological Survey confirmed with the Center for Data Innovation over email on July 22, 2019, that "1-meter DEMs are available or in progress for 45% of the country."

HOSTKEY deploys a well-established environment for machine learning applications such as neural networks with high-performance GPUs and dedicated servers with NVIDIA GTX 1080/1080Ti and RTX 2080Ti graphics cards. Just start your TensorFlow experience in a straightforward and user-friendly environment making it easy to build, train and deploy machine learning models at scale. TensorFlow runs up to 50% faster on our high-performance GPUs and scales easily. Now your machines learn in hours, not days. Deep Learning is a buzzword that will be familiar to most people.

Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for efficient allocation of wireless resources. Obtaining Down-Link (DL) CSI in Frequency Division Duplexing (FDD) systems from Up-Link (UL) CSI is not as straightforward as in TDD systems, and so usually users feedback the DL-CSI to the transmitter. To remove the need for feedback (and thus having less signaling overhead), several methods have been studied to estimate DL-CSI from UL-CSI. In this paper, we propose a scheme to infer DL-CSI by observing UL-CSI in which we use two recent deep neural network structures: a) Convolutional Neural networks and b) Generative Adversarial Networks. The proposed deep network structures first learn a latent model of the environment from the training data. Then, the result latent model is used to predict the DL-CSI from the UL-CSI. We have simulated the proposed scheme and evaluated its performance in a few network settings. Simulation results (for different multipath environments) demonstrate efficiency of both direct and generative approaches for UL2DL prediction. One key feature of new generation of cellular networks is their efficient use of frequency bands and energy. To achieve this goal, they use various techniques such as water-filling, appropriate precoding and beamforming. In Time Division Duplexing (TDD) systems, Up-Link (UL) and Down-Link (DL) frequencies are equal, so we can use channel reciprocity and simply infer the DL channel by observing the UL channel.

Kleos Space S.A., a space-powered Radio Frequency Reconnaissance data provider, has signed a binding MOU with EarthLab Luxembourg S.A. to examine collaboration opportunities for the use of Kleos RF Reconnaissance and Geolocation data for the insurance sector and for other different geospatial intelligence (GEOINT) purposes. EarthLab was the first European centre established for environment monitoring dedicated to industrial and environmental risk. With partners Telespazio France, e-GEOS, the wholly state-owned postal and telecommunications company, POST Luxembourg and the SME HITEC Luxembourg, through its Max-ICS platform it provides earth observation, geo-spatial information and risk assessment data analytics to improve operational and strategic decision making for the insurance, reinsurance, investment fund industries and the related market segments. Kleos and EarthLab aim to utilise geolocation and activity-based intelligence data from the Kleos' Scouting Mission* and further Kleos satellite launches to enhance EarthLab's geospatial intelligence analytics, allowing EarthLab to verify radar detections and optical observations. Collaboration opportunities include the opportunity to develop a value-add proposition for current and future EarthLab Maritime business line prospects using Kleos data, develop a commercial procurement strategy and the potential to implement two laboratory scenarios to validate the technical usability of the data and the mutual commercial strategy.

Applying Machine Learning (ML) techniques to design and optimize computer architectures is a promising research direction. Optimizing the runtime performance of a Network-on-Chip (NoC) necessitates a continuous learning framework. In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance. We present three RL-based methods for learning optimal routing algorithms. The experimental results show the algorithms can successfully learn a near-optimal solution across different environment states. Reproducible Code: github.com/huckiyang/interconnect-routing-gym

We are excited to announce our third annual PHLAI conference. The theme of this year's conference is using artificial intelligence and machine learning to improve the customer experience. From improving the efficiency of the customer experience to generating new insights and building deeper relationships with customers, artificial intelligence and machine learning is transforming the way companies around the world engage with their customers. At Comcast, we see artificial intelligence and machine learning as powerful tools for improving the customer experience. Artificial intelligence and machine learning is empowering our agents and enabling self-service tools, like the Xfinity Assistant, to provide smarter, more efficient interactions.

Chinese technology company Huawei launched its Global Industry Vision (GIV) report that identifies 10 megatrends shaping how we live and work. Drawing from Huawei's own quantitative data and real-world use cases of how intelligent technology is permeating every industry, the report also predicts technology trends up until 2025. GIV predicts a 14% global penetration rate of home robots. GIV predicts that the percentage of companies using AR/VR will increase to 10%. Future searches will be button-free, personal social networks will be created effortlessly, and industry will benefit from "zero-search maintenance".