Intel® Modular Edge Compute Architecture
Intel® Modular Edge Compute Architecture evolves the concept of traditional edge products and enables customers to scale up their edge compute on-premises. Creates flexibility, velocity for edge deployments, and extends the ability for multiple form factors to become an edge server – rack and beyond. Lowers TCO, increases interoperability via specification standards, provides an edge platform for innovation that scales to deliver on your business edge needs.
Edge Computing: A Rising Opportunity
According to McKinsey, edge computing hardware represents a potential value of $175 billion to $215 billion by 2025 in multiple industries. Edge computing hardware value includes opportunity across tech stack (the sensors, on-device firmware, storage, and processors).
The Value of Workload Consolidation with Edge Computing
Workload consolidation transforms edge computing through reducing costs, increasing operational efficiency, and enabling agility by:
- Eliminating dedicated, costly hardware components
- Increasing the range of hardware choices, reducing total cost of ownership, and providing hardware interoperability and platform flexibility
- Simplifying architecture with hardware failover support
- Providing faster repair times
- Upgrading the system seamlessly
The Benefits of a Modular Approach
Reduces space, noise, and cable complexity
- Reduces the number of cables through integrated networking in the system.
- Supports low noise requirements and decreases noise by more than 30% over a standard rack-based system.
Provides scale up and hybrid processing, inferencing, and integrates multiple devices and components
Integrates networking and supports hybrid mode CPUs (Intel® Core™ and Intel® Xeon® processors) with 2-6 CPU and PCIe lanes.
Provides hardware interoperability and software integration out of the box with solution providers
Integrated HW/SW solutions and support for industry/geo specific applications and needs across Retail, Banking, Hospitality, Restaurants, and Education.
- Pre-validated with Edge Software Hub Vision reference SW & Smart Edge.
- Edge to Cloud ready (Azure IoT Edge & AWS Edge).
- Solution provider ready, MEC Arch lab enables multiple Edge SW & Solutions to validate at scale.
Edge Hardware Products
Advantech USM-600
Compatible with Dual CPU boards for 8th/9th gen Intel® Processors for multiple edge AI tasks. Embedded with Intel® Core™ i3-9100TE, Intel® Core™ i5-9500TE, and Intel® Core™ i7-9700TE processors while housing two LAN, four HDMI, and six USB 3.1 I/O ports. Storage support for up to two M.2 SSDs and 4 SATA HDDs slots and Raid 0, 1, 5 options. Two PCIe x16 slots and one PCIe x8 along with two M.2 E-key for Wi-Fi/LTE expansion, making it ideal for Intel® Movidius™ card or graphics card for vision and imaging applications.
Portwell IWOS Series
Touting support with 2-4 compute modules on Intel® Xeon® E-2278GE, Intel® Core™ i7-9700E, Intel® Core™ i5-9500E, and Intel® Core™ i3-9300E processors, with dual channel DDR4 SO-DIMM 2666 and up to 32GB per module, as well as two HDMI displays and two USB 3.0 ports per module. The solution is optimized with Intel® Virtualization Technology, the Intel vPro® platform, and the Intel® SGX platform to best support virtualization/containerization, remote manageability, and enhanced code/data security.
Industries & Use Cases
Retail
In Retail, customer experience, convenience, and operational efficiency are key considerations for deploying compute on the edge. Low latency and split-second responses are important for realizing benefits at the edge. With data collection being generated in-store, this enables:
- Quick check-outs via frictionless/automated transactions.
- Efficient demand planning to manage supply/demand quickly, including rapid stock replenishment, tracking high-value products, and preventing shrinkage.
- Increased revenue through in-store recommendations, taking the form of interactive and pertinent location-based ads.
Banking
In Banking, data security, privacy, and storage are vital considerations for deploying compute on the edge. Customer service can be significantly improved regarding in-branch deployment. Functions that benefit from low latency include:
- Trading analytics
- Frictionless banking
- Payment systems augmented with edge computing for enhanced security
Hospitality
In Hospitality and Quick Serve Restaurants (QSR), the size of operations and infrastructure are the two big considerations for deploying compute on the edge. Things like how critical latency affects the efficiency of these operations is an important factor. Such benefits include:
- More open operations (POS, Reservation, Delivery, etc.), which are often times siloed functions in restaurants. Adding edge infrastructure enables customers to connect all these systems data for improved business intelligence.
- Better in-store customer experiences, creating an advantage over other players in the market.
- Operational efficiencies with real-time tracking of guest experiences, employee operations, and personalized experience for guests/customers depending upon their past choices.
- Energy and water savings through things like waste reduction during food production/storage and more efficient food inventory management.
Education
In Education, universities are employing "middleware" tech for monitoring, storing, and filtering incoming data enabling. Plus, K-12 institutions are implementing stronger levels of security. Edge computing across Education enables:
- Research through progressive filtering and intelligent analytics.
- Improved security via campus emergency audits, student tracking, and malware protection.
- Enhanced classroom instruction with improved student learning experiences and monitoring.
Frequently Asked Questions
It is the foundation for off-the-shelf or specialized scalable IoT systems and solutions that have vertical-specific applications and on-premises edge solutions with network connections.
It is a reference architecture specification that evolves the concept of traditional edge products and enables customers to scale up their edge compute on-premises.
It creates flexibility, velocity for edge deployments, and extends the ability for multiple form factors to become an edge server on the rack and beyond.
Our goal is to drive adoption of Intel® tech (HW, SW) with optimized compute and workload performance close to where data is actually generated.
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