Emerging data-driven technologies like the Internet-of-Things (IoT), Artificial Intelligence (AI), Augmented and Virtual Reality (AR/VR) and Blockchain dominate a lot of conversations in the tech industry. Organizations mapping out their digital transformation journey are faced with understanding how these emerging technologies fit into their strategies. One important consideration for these technologies is where they overlap or can be used in conjunction to achieve business outcomes. For example, where AI meets IoT, sometimes referred to as the Artificial Intelligence of Things (AIoT), provides for capabilities beyond the individual adoption of either technology. AIoT delivers an avenue for networks of connected devices to gather vast amounts of data from the physical world, and with programmable intelligence, learn to reason and process this data into information and insights, then act on it the way humans do.
The intersection point of the physical and digital worlds is what we call the ‘Edge’ at Dell. It is the point where data is generated, gathered, and processed to create new value, and it is as different as the industries that define Edge use cases. The Edge presents new challenges to the way the IT industry does computing. Where we’ve historically relied on traditional data centers and cloud computing to process data generated outside of data centers, the need to derive real time value at the point of generation necessitates the presence of computing resources at the Edge. This has led to the emergence of Edge Computing. Edge Computing drives value for organizations by accelerating both the discovery of insights from data and the digitization of key business processes. It also allows businesses to redefine their end customer’s experiences.
An interesting consequence of Edge Computing as it pertains to the Artificial Intelligence of Things, is that it becomes the vehicle which brings intelligence (AI) closer to the source of data generation (interconnected things). In the new age of Edge computing, compute platforms at the Edge will provide lightweight designs that can be successfully deployed despite spatial, environmental, power, and connectivity constraints. These designs can be secured and can support applications that require insights at the speed of real time.
Organizational strategy for Edge will differ greatly as no two deployments will be the same. Perhaps that strategy will involve starting with the low hanging fruit of projects that are easiest to implement. This might be similar to the initial steps of an AI strategy as described here. Defining the Edge in terms of business outcomes will bring a level of clarity. For example, wind farmers who need to monitor the health of windmills will define that Edge much differently than the dairy farmer who wants to monitor the health of their cows. Even though the use case is similar, subtle differences around ‘what’ is being monitored will affect how the technology deployments will occur; down to data architectures, platform of choice, data processing and security at the Edge. As with most adoptions of new technology, deploying at the Edge should have measurable milestones and quick wins on the journey to success.
At Dell Technologies we deliver solutions that support entire data lifecycle management via consistent ecosystems that scale at the Edge and span on-prem and off-prem clouds. We consider this critical due to the costs that make it difficult to capture, analyze and extract value from untapped data sources. Our solutions are built to meet the demands of operational and physical constraints that define our customer’s ‘Edge.’ In addition, we have been particular about our global ecosystem and support network to ensure our customers have access to expertise and best practices. Lastly, we deliver our platform solutions with intrinsic security and reliability; we understand that the increased exposure to threats at the Edge presents additional risks, making security a constant concern for organizations looking to grow their Edge deployments.
For platforms that bring intelligence to the Edge, our PowerEdge XE2420 performs AI inferencing as efficiently as benchmarked 2U rack servers, with its support for NVIDIA GPUs in a short depth server that was designed to provide dense compute, simplified management and robust security for harsh Edge environments. Its inference performance testing has been carried out using MLPerf benchmarks.
Our PowerEdge R7525 and DSS 8400 platforms have also been ranked and benchmarked for Medical Imaging, NLP, Image Classification, Speech Recognition, Object Detection, and Recommendation, under performance per accelerator and inference testing with NVIDIA A100, T4 and Quadro RTX GPUs. The wide selection of servers and accelerator options and use cases we support as benchmarked solutions is evidence of our commitment to meeting the needs of our customers at their unique Edge.
The Edge is now being reinvented with technologies like AI, IoT, and 5G, which bring added value to business outcomes. For more about our Edge Computing portfolio and all the ways they enable Edge use cases, visit our portfolio page.