Experts agree, the more data an AI engine can ingest and process, the better its algorithms and inferences become. That's why with traditional AI infrastructure, the goal has always been to feed the system with as much data as possible.
According to Gartner, however, by 2025, 75% of all data will be generated in places outside of traditional data centers. Therefore, the critical challenge for solution providers is how to continue to empower and grow their AI applications in this changing environment. Rest assured, the answer lies in Edge-Based AI servers that expand the AI function from the core and cloud outward to the Edge.
The case for AI's expansion to the Edge
AI in the Cloud
In the cloud, AI benefits from the macro-level view of data. That's because cloud data centers can provide the massive amounts of storage and compute power needed for AI algorithms and inferences.
AI at the Edge
That said, thanks to mobile devices, wearables, and other IoT technology, more and more data is being generated at the Edge - data that is prime for AI-enabled applications. For example, end-users leverage AI to assist with inventory management, personalized shopping experiences, and real-time security in the retail industry.
And another burgeoning area where AI at the Edge is growing is healthcare. Given AI's ability to connect various data forms, hospitals combine disease data with medical imaging to diagnose and treat diseases quicker than ever.
In both cases, Edge-Based AI servers work in concert with cloud and core-based platforms to create applications that accomplish more than ever. The list of new Edge-Based AI applications grows every day - powered by a new generation of hardware. And the possible use cases of AI at the Edge don't stop there.
Edge-to-Cloud architecture - important priorities
While traditional AI infrastructure will have a vital role, newer platforms on the Edge, working in concert with the cloud, are where the growth will occur. They will provide the foundation for the latest, AI-enabled applications. First solution providers need to consider their priorities when building such an architecture.
Edge-to-cloud architecture has distinct needs, namely:
High performance - AI, especially in locations where inference and training occur, requires robust compute performance - especially given the number of computations required.
Low latency - Given that applications on the Edge can leverage data in real-time, edge-based AI must take advantage of local platforms, storage, and networks to provide the best possible performance.
High capacity - With the enormous amount of data that will be processed by tomorrow's AI's, Edge-to-Cloud infrastructure must provide the right amount of processing and storage power wherever and whenever needed.
Security - Especially in health care or financial use cases, AI engines work with large volumes of sensitive data. Therefore, the hardware they run on must be secure from Edge-to-Cloud, preventing unauthorized use.
Edge-to-Cloud architecture - essential hardware technologies
Solution providers need to build a robust Edge-Based infrastructure to accommodate the above needs. Fortunately, Intel offers multiple innovations, each of which can power AI forward in a unique way.
Intel 3rd Generation Xeon Scalable Processors with Built-in Deep Learning Boost - With this latest generation of revolutionary processors, Intel-based AI platforms can perform inferences up to 30x faster than the previous generation. This improvement enables end-users like Audi to leverage AI to boost auto inspections by 100x.
Intel's Field Programmable Gate Arrays (Intel FPGAs) - Another way to accelerate AI workloads is with FPGAs. One environmental agency known as Project CoRail, with the help of Intel FPGAs, is monitoring coral reefs with cameras instead of human divers. In doing so, they lower the risk of disturbing the reef's delicate ecosystem.
Intel Optane Solid State Drives (Intel SSD) - Intel SSDs are used at the Edge to ingest, organize, and distribute data to AIs in real-time. For the Montefiore Health System, this high-performing storage allows their AI to identify at-risk patients more quickly, reducing costs and improving quality of care.
Intel Optane Persistent Memory (PMem) - By creating a non-volatile source of high-speed memory, PMem enables solution providers to store only the hottest data in server memory and offload the rest to scale-out storage. For example, one data service provider cut the latency of its storage by 80%.
Intel security - To ensure that Intel-based platforms are as secure as possible, Intel offers a variety of functions. For example:
- Intel Software Guard Extensions (Intel SGX) protect the code running on Intel processors even if the OS is compromised.
- Intel Control-Flow Enforcement (Intel CET) provides CPU-level protection against malware attacks.
- Intel Total Memory Encryption (Intel TME) provides full system memory encryption.
Edge-Based AI - how to get started
If you're ready to leverage AI at the Edge and take your solution further, your next step is to partner with a skilled systems integrator.
As an Intel Technology Provider, UNICOM Engineering has helped drive the latest solutions with our partners for decades. Our skilled team actively builds solutions based on the latest 3rd Gen Intel Xeon Scalable processors, Intel Optane persistent memory 200 series, Intel SmartNIC and Ethernet 800 Series Network Adapters, and Intel Optane SSDs. Our customers benefit from solutions optimized for telecom, cloud, enterprise, network, security, IoT, and HPC workloads with expanded I/O, storage, and network connectivity options by leveraging our services. Learn more about how UNICOM Engineering can help you transition to next-gen solutions by scheduling a consultation.