According to theAmerican Trucking Associations(ATA), at current trends the driver shortage could surpass 160,000 by 2030. ATA estimates that, in the next decade, the industry will have to recruit nearly a million new drivers into the industry to replace drivers exiting the field due to retirements, driver burn-out, compensation and poor benefits. These are the challenges facing transportation executives in securing a robust driver pool.
However, the challenge of driver shortages does not end with the trucking industry. Rather, the scarcity of drivers directly affects the larger manufacturing sector.
System design requirements are well understood for high-performance artificial intelligence applications destined to reside in enterprise or cloud data centers. Data centers are specifically designed to provide a clean, cool environment with stable and standard power with no need to worry about vibration or shock loads.
Most AI inferencing requirements are outside the datacenter at the edge where data is being sourced and inferencing queries are being generated. AI inferencing effectiveness is measured by the speed and accuracy with which answers are provided, and, in many applications, real-time response is required. To meet the objectives of many AI applications, a very large number of inferencing queries need to be serviced simultaneously. Often many different inferencing models answering different types of queries need to be coordinating in parallel.
A long-haul truck cruises by you on Interstate 10 in west Texas. You get a brief wave from the driver who seems unfocused on the road ahead. It’s hot and the road is bumpy. You didn’t realize it but you just encountered an autonomous driving truck in development, testing out the latest version of its Artificial Intelligence algorithms. The truck is loaded with unseen video cameras, lidar, radar and infer-red sensors. As it travels along it is seeing its environment and its on-board computers are making the thousands of little decisions that keep it moving safely to its prescribed destination. The driver is a safety layer that will be removed in future iterations of the design.
Adoption of the latest HPC and AI technologies in autonomous vehicles is creating a surge in vehicle real-time data processing capabilities. The growth in number, speed and resolution of vehicle sensors and related compute performance in autonomous vehicles has in turn led to increased demand for high-capacity, high-throughput storage. Effective storage within autonomous vehicles must meet three primary criteria: throughput to match the capture and processing rates, a rapid data transfer workflow for offloading the captured data, and a rugged environmental design to operate in any autonomous vehicle conditions.
Notable achievements in 2021: One Stop Systems won several programs to provide data-center-class AI hardware into rugged mobile edge environments without compromising performance such as inMobile Datacenters and Autonomous Trucks. It also launched the high performance Rugged Compute Server for AI Transportable applications,Rigel.
OSS released its flagship artificial intelligence (AI) transportable compute server, the Rigel Edge Supercomputer, according to the company this month.
With a dense form factor, the Rigel is designed for deployments in "tight spaces" at the edge, such as an equipment bay of autonomous vehicles, within mobile command centers, under seats of helicopters, or in an aircraft equipment bay.
COVID-19 may have put the brakes on a lot of industries but things are up and running in Escondido. A recent study by the San Diego Regional Economic Development Corporation, found that the 78 corridor, which includes Escondido, provides over 40,000 manufacturing jobs and has an annual economic impact of $18 billion. These jobs include computer and electronics manufacturing. The study found this work is projected to rise up to 6% in the next five years.
Surviving a fast-moving battlefield situation, protecting sensitive industrial or transportation hub assets, or ensuring uninterrupted operation of critical national infrastructure, intelligent long-range surveillance is critical. OSS provides 24/7 remote long range threat detection and situational awareness coupled with human-machine control allows a fast and appropriate threat response that is fundamental to addressing these security imperatives.
Many amazing advances in technology over the last decade have transformed industries, disrupted verticals and created tremendous new market opportunities. Addressing the surge in data, companies are looking to new technologies that can provide greater storage capacity, as well as support greater data processing and management capabilities. Edge computing delivers these advantages by bringing the performance of the data center close to the source generating the data. Sophisticated hardware can bring computing performance and the power of the data center at the transportable Edge for a range of applications, including air travel, logistics, distribution, military and robotics.
High-performance edge computing firm One Stop Systems, Inc. is looking to accelerate growth in 2021 by tapping in to demand for edge AI. Implementing artificial intelligence at the edge is a hot industry topic, and it's the focus of a lot of technology development and investment. OSS plans to make inroads by focusing on a segment of the edge computing market that executives are calling "AI Transportables."
Ron Javor, Product Marketing Manager at OSS highlights one of the newest products to the AI Transportables portfolio. A building block product to enable HPC at the edge. The OSS-3U-SDS provides PCIe Gen4 HPC in a rugged, compact form-factor with flexibility in scale-up or scale-out.