"QC Ware estimates that with Forge Data Loaders, the industry's 10-to-15-year timeline for practical applications of QML will be reduced significantly," said Yianni Gamvros, Head of Product and Business Development at QC Ware. "What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines." Apart from the Forge Data Loaders, the latest release of Forge includes tools for GPU acceleration, which allows algorithms testing to be completed in seconds versus hours, and turnkey algorithms implementations on a choice of simulators and quantum hardware. Quantum hardware integrations include D-Wave Systems, and IonQ and Rigetti architectures through Amazon Braket.
Increased Integration of Different Solutions to Provide Improved Performance 184.108.40.206 Rapid Industrial Growth in Emerging Economies 5.2.4 Challenges 220.127.116.11 Threats Related to Cybersecurity 18.104.22.168 Complexity in Implementation of Smart Manufacturing Technology Systems 22.214.171.124 Lack of Awareness About Benefits of Adopting Information and Enabling Technologies 126.96.36.199 Lack of Skilled Workforce 5.3 Industrial Wearable Devices Trends in Smart Manufacturing 5.3.1 By Device 188.8.131.52
There is a benchmark of desktop and laptop GPU cards for deep learning: AI Benchmark. You can run these tests yourself, see https://pypi.org/project/ai-benchmark/. Take note that some GPUs are good for games but not for deep learning (for games 1660 Ti would be good enough and much, much cheaper, vide this and that). For general benchmarks, I recommend UserBenchmark (my Lenovo Y740 with Nvidia RTX 2080 Max-Q here.) For comparison of different cards between frameworks, see Performance in: Keras or PyTorch as your first deep learning framework (June 2018), based on Comparing Deep Learning Frameworks: A Rosetta Stone Approach.
This is a pretty active area of research, namely "edge device computing" which often intertwines with "model compression". Using embedded devices that have GPUs such as the Nvidia Jetson TX2 is often a good place to start. This way you can use a smaller GPU that offers CUDA support in an embedded setting. However you must make sure your models are small enough to fit on a device with compute limitations. Frameworks like Tensorflow can train models on a GPU and then you can save the weights, then perform inference elsewhere on a CPU, perhaps you can do something like this on a raspberry pi but keep in mind you will be severly limited on such a device.
Entitled "Bringing AI and Intelligent Live Streaming to the Smart City," this presentation will be led by key members of CrowdOptic's technical team: Richard Smith, VP of Product, Austin Markus, VP of CrowdOptic Labs, and Joshua Davis, Principal Director of Engineering. There are already hundreds of thousands of cameras in many smart cities, but how intelligent are their video cameras and video management systems? This session will dig into the use of artificial intelligence to control cameras, and will explain how sensor data can be used to analyze video stored at the edge. CrowdOptic Intersect APIs expose developers to triangulation and cluster detection algorithms, guiding them through the basics of how CrowdOptic works with camera lines of sight to bring artificial intelligence to the smart city. A quick demonstration will drive home the depth of these APIs, showing how smart phones leverage cameras in the smart city to effectively look through walls and around corners.