Until recently, hyperautomation was used almost exclusively in enterprise environments to turbocharge business process automation and thereby streamline operations. The complexity of combining technologies such as artificial intelligence (AI), machine learning (ML) and robotic process automation (RPA) initially limited use of this “Automation 2.0” capability to large organizations, but that promises to change in 2022.
As workplace shortages and operational costs continue to increase – and as hyperautomation enters its third year on Gartner’s list of top technology trends – the intelligent digital workforce will move beyond the data center to enable new efficiencies at the edge. Inferencing applications in this category will span intelligent patient safety monitoring for hospitals, advanced analytics in video surveillance, automated food preparation and transferring, autonomous forklifts in factories, and more. Hardware appliances will be purpose-built for operating machinery, and more applications will rely on platforms designed to be mobile.
Four key factors have converged to pave the way for this transition.
1 - Lower-cost AI acceleration: Improved AI/ML price-to-performance is making edge-based inferencing more affordable. Core components are getting smaller, cheaper and faster. Solutions like NVIDIA Jetson, Google Coral, and Hailo are leading the charge with high-efficiency, small form-factor embedded computing boards and acceleration modules designed to run at the edge.
And the improvements keep coming. Much more powerful AI accelerator cards as well as SoC AI solutions are coming to market. As a result, solutions like NVIDIA’s Jetson will soon have enough power to replace some GPU-based edge solutions. This will slash the cost of ownership for an inference machine from a range of $2,500 to under $1,000. Advances like these are removing barriers to development of new products that would have been impractical to bring to market even 12 months ago.
2 – Onboard AI processing abilities: Thanks to these upgrades in chip technology, quality onboard AI acceleration is being designed into the CPU. As an example, Intel’s new Alder Lake platform provides AI acceleration through DL Boost, a combination of VNNI instructions on the CPU and GPU acceleration for AI inferencing of high resolution image workloads with the OpenVINO toolkit. That means that inferencing one or two models at the edge is now possible without an additional accelerator card, keeping costs low and eliminating latency and bandwidth constraints because the cloud is not needed for inferencing. This will enable edge-based AI deployments in any environment and even at scale.
In a hospital setting, for example, every room can now be furnished with a mobile cart outfitted with a camera, embedded workstation and AI software that can detect activity such as an imminent fall without streaming data to the hospital network. Visitors’ body temperature can be screened at hospital entrances without physical contact. Virtual patient interactions can be conducted with speech recognition. Surgeons can control and navigate medical images with speech and hand gestures. These kinds of applications can help combat labor shortages in healthcare as well as other industries, and onboard AI acceleration improvements makes them even more affordable.
3 – Fast-tracked machine learning: Equally important in fueling the adoption of edge-based hyperautomation is the emergence of machine learning frameworks like NVIDIA Clara Guardian, Google TensorFlow and Intel OpenVINO that can dramatically reduce development time. These building blocks make it faster and easier to implement machine learning by providing libraries of tools such as pre-built training models that can eliminate months of work.
Again, consider smart hospital applications designed for deployment at the edge. NVIDIA Clara Guardian includes pre-trained machine learning models in areas such as body pose, gesture recognition, heart rate estimation, mask detection, and speech recognition for common patient requests in a healthcare setting, as well as tools that make it possible to securely manage AI deployments across multiple servers and edge devices. These resources slash AI development time by a factor of 10, making the effort more financially attractive to ISVs.
4 – Hardware building blocks: Complementing these software tools are new mobile device and reference platform options that further shrink both cost and time to market by eliminating the need for custom hardware design that can require months of prototyping and cost tens of thousands of dollars.
MBX Systems, for example, offers an exclusive mobile cart platform enabling a single housing to be quickly customized to meet different ISV needs, using workstations that are pre-certified for medical and global use, and consolidating all development and assembly services under one roof. The company also offers a portfolio of deployment-ready hardware reference platforms for AI applications being delivered as embedded systems, edge devices or edge servers.
Taken together, these factors will help turn 2022 into a banner year for edge-based hyperautomation solutions. As a result, hyperautomation will become more than an efficiency enabler for enterprise IT operations and instead provide a whole new range of opportunities for bringing cutting-edge AI into the mainstream.
View the original article on VMBlog.