Leicester sacks manager Ranieri

first_imgLONDON (AP): Claudio Ranieri was fired as the manager of English champion Leicester yesterday, nine months after guiding the club to the most improbable title triumph in Premier League history. Leicester’s Thai owners took the drastic measure with the team just a point above the relegation zone having 13 games remaining in a dreadful title defence. “We are duty-bound to put the club’s long-term interests above all sense of personal sentiment,” Leicester vice-chairman Aiyawatt Srivaddhanaprabha said, “no matter how strong that might be.” Leicester, with a team of journeymen, cast-offs and previously unheralded players, won the Premier League last season at odds of 5,000-1, a feat generally viewed as one of the greatest in all sports. Ranieri was last month voted as FIFA coach of the year. However, the Italian manager has come under heavy pressure in recent weeks as Leicester slipped closer to the bottom three in the Premier League and reportedly fell out with some of his players.last_img read more

3 arrested after high-speed Police chase

first_imgAfter almost an hour of chasing a suspicious car with fake registration plates, the Police took three men, including an ex-Guyana Police Force (GPF) officer, into custody on Monday morning.One of the suspects being escorted by PoliceDuring interrogations, two of the men gave their identities as Eloy Livan, 28, of Lot 47 Richards Town, Lusignan, East Coast Demerara and Devon Chase, 30, of Hill Street, Albouystown. The third suspect refused to cooperate with the Police.Reports are Police ranks observed a heavily tinted motor car with a suspected fake number plate on Regent Street, Georgetown and approached it. However, upon recognising the Police, the occupants of the motor car drove away and the ranks gave chase.After driving through several streets in Georgetown, the driver of the tinted vehicle lost control and ended up in a ditch.The real registration number was covered by a fake registration numberThe Police have since recovered a 9mm pistol and 10 live rounds of ammunition. Police are trying to ascertain the owner of the vehicle, and will also conduct a ballistic test on the gun to determine if it was used to commit any crimes.last_img read more

Push-button generation of deep neural networks

first_img antedeluvian says: 3 thoughts on “Push-button generation of deep neural networks” The term “deep learning” refers to using deep (multi-layer) artificial neural networks to make sense out of complex data such as images, sounds, and text. Until recently, this technology has been largely relegated to academia. Over the past couple of years, however, increased computing performance coupled with reduced power consumption and augmented by major strides in neural network frameworks and algorithms has thrust deep learning into the mainstream.When I attended the Embedded Vision Summit recently, for example, I saw an amazing demonstration of machine vision in which a deep neural network (DNN) running on an FPGA was identifying randomly presented images in real time (check out this column to see a video). As an aside, one of the best lines I heard at the summit was “You can’t swing a dead cat in here without some deep learning system saying ‘Hey, that’s a dead cat!’ “ But we digress…As another example, take a look at this column describing how researchers at MIT used a deep learning algorithm to analyze videos showing tens of thousands of different objects and materials being prodded, scraped, and hit with a drumstick. The trained algorithm could subsequently watch silent videos and generate accompanying sounds sufficiently convincing to fool human observers.(Source: CEVA) Two of the most popular frameworks for deep learning are Caffe-based networks and Google’s TensorFlow-based networks. Caffe is a well-known and widely-used machine-vision library that ported Matlab’s implementation of fast convolutional nets to C and C++; it was created with expression, speed, and modularity in mind; and it’s primarily employed by academics and researchers with some commercial use. TensorFlow is a relatively new alternative to Caffe that is supported and promoted by Google; it features a software library for numerical computation using data flow graphs; and it’s scalable and applicable to both research and commercial applications.Caffe was designed for image classification and is not intended for other deep-learning applications such as text or sound. By comparison, TensorFlow has been created from the ground up to address a wide range of target applications.The original deep learning frameworks supported only linear networks. Modern frameworks, like TensorFlow, support more sophisticated topologies involving multiple layers per level and multiple-input-multiple-output.(Source: CEVA) There are several steps involved in creating a deep neural network. The first is to define and implement the network architecture and topology. Next, the network undergoes a training stage, which is performed offline on a powerful computing platform using tens or hundreds of thousands of images (in the case of a machine vision application). The result is a floating-point representation of the network and its “weights” (coefficients).(Source: CEVA) The final step is to take the floating-point representation of the network and its weights and transmogrify it into a fixed-point equivalent suitable for running on a target platform.All of which brings us to the fact that CEVA has just announced the second generation of its CEVA Deep Neural Network (CDNN2). CDNN2 is a neural network software framework for machine learning that features the CEVA Network Generator. In turn, the CEVA Network Generator can take a floating-point representation of a network — Caffe-based or TenserFlow-based (any topography) — and transmogrify it into a small, fast, energy-efficient fixed-point equivalent targeted at the CEVA-XM4 intelligent vision processor (the CEVA-XM4 can be realized as a hard core on an SoC or as a soft-core on an FPGA).(Source: CEVA) CDNN2 supports the most advanced neural network layers in use today, including the following:Input manipulation layer (pre-process stage resize, jittering and more)ConvolutionalNormalizationPooling (Average and Max)Fully ConnectedSoftmaxActivation (ReLU, Parametric ReLU, TanH, Sigmoid)New: DeconvolutionNew: ConcatenationNew: UpsampleNew: ArgmaxNew: Custom user layer attaching a specific functionalityThe folks at CEVA boast that taking a floating-point network, transmogrifying it into its fixed-point equivalent, loading it into a CEVA-XM4 engine, and running it is a “push-button” approach. Of course, we’ve all seen (sometimes given) demonstrations involving a little slight-of-hand and “Here’s one I prepared earlier,” so the guys and gals at CEVA have prepared this video showing the entire process in a single (less than 10-minute) shot.CDNN2 is intended to be used for object recognition, advanced driver assistance systems (ADAS), Artificial intelligence (AI), video analytics, augmented reality (AR), virtual reality (VR) and similar computer vision applications.Coupled with the CEVA-XM4 intelligent vision processor, CDNN2 offers significant time-to-market and power advantages for implementing machine learning in embedded systems for smartphones, advanced driver assistance systems (ADAS), surveillance equipment, drones, robots and other camera-enabled smart devices.The CDNN2 software library is supplied as source code, extending the CEVA-XM4’s existing Application Developer Kit (ADK) and computer vision library, CEVA-CV. It is flexible and modular, capable of supporting either complete CNN implementations or specific layers for a wide breadth of networks. These networks include Alexnet, GoogLeNet, ResidualNet (ResNet), SegNet, VGG (VGG-19, VGG-16, VGG_S) and Network-in-network (NIN), among others.As noted earlier, CDNN2 supports the most advanced neural network layers, including convolution, deconvolution, pooling, fully connected, softmax, concatenation, and upsample, as well as various inception models. All network topologies are supported, including Multiple-Input-Multiple-Output, multiple layers per level, fully convolutional networks, in addition to linear networks (such as Alexnet).Click Here for more information on CEVA, CEVA-XM4, and CDNN2. Log in to Reply Log in to Reply elizabethsimon says: Log in to Reply Share this:TwitterFacebookLinkedInMoreRedditTumblrPinterestWhatsAppSkypePocketTelegram Tags: Automotive, Communications, Consumer, Industry Clive”Max”Maxfield says: July 1, 2016 at 4:07 pm “I think it’s not so much that they are smarter. It’s just that they have devoted a lot of time to figuring these things out.nnThe way I look at it, it takes folk like us to turn this stuff into real products.nnAs much fun as it would be to work on som Continue Reading Previous How to run your own secure IoT cloud server for $8/yearNext Metrics we need – Part 2 “I know what you mean — I look at today’s DSP algorithms and they make my eyes water — but then I think to myself that I can do anything I set my mind to … it’s just that I don’t have the time (I believe anything I tell myself :-)” “All I can say is that there are lots of people way smarter than I am. Going to shows like EVS leave me with a massive inferiority complex. “ June 30, 2016 at 10:26 pm July 1, 2016 at 5:41 pm Leave a Reply Cancel reply You must Register or Login to post a comment. This site uses Akismet to reduce spam. Learn how your comment data is processed.last_img read more