HP Innovation Journal Issue 12: Summer 2019 | Page 67

Developing algorithms and dealing with errors by hand is expensive. simple problems can be trained quickly, and problems that are otherwise impossible just take longer. VIRTUAL MACHINES AND FACTORIES The rise of cyber-physical systems with volumes of machine-generated data enable virtual machines (VMs). A VM, also called Digital Twin in the industry, is a holistic, model-based representation of a physical system with all of the functional physical attributes that mimic the operation of the machinery. An example of a physical system is a 3D Multi Jet Fusion printer. During development, VM system-scale simulations can reduce time for product development by 33%. 5 VM facto- ry-scale simulations of deployment environments, such as additive manufacturing operations, can help customers predict the total cost of ownership (TCO) based on vari- ous workflows and service level agreements. At runtime, data collected in the factory and VM models enable customers to drive real-time efficiency, strive to main- tain the TCO for various material and energy flows that are part of manufacturing, and get to root causes of anomalies. COMPUTE’S BRAVE NEW WORLD As an industry, we quest tirelessly for technologies that enable us to work more efficiently, sustainably, and productively. Innovation does not happen in a vacuum, and often advancements in one area (IoT, sensors, monitors) cause complexities downstream (the resulting deluge of data). HP’s ability to research and tackle challenges from multiple angles at once often results in branched but com- plimentary approaches—like the three areas of innovation described above. Turning raw data into actionable infor- mation has always been important, but perhaps never in our history has the available data been vaster and the need to process it more critical. With rising energy demands, we’ll increasingly turn our focus toward lowering con- sumption and maximizing efficiency. In this effort, and in many others, new compute architectures and technologies are essential building blocks for our data-driven future. 1. A network edge is defined as one or fewer network hops from the source of the data. 2. Evidence of this includes delays in Intel’s 10nm (nanometer) CPUs. 3. Next-generation 4nm transistors are hardly wide enough for 20 silicon atoms. 4. IDC 5. Reductions are achieved by reducing physical iterations, and VMs enable “what if” analysis of new future components. 65