Moving Intelligence from Cloud to Edge
As visitors start convening in Nuremberg for embedded world, there is a perfect storm brewing in embedded systems development, using the parallel rise of cloud native computing coupled with growth in intelligent, connected IoT devices and edge technologies.
It was paraphrased well in a recent briefing with Remi EL Ouazzane, president from the microcontrollers and digital ICs group at STMicroelectronics commented, by which he talked about the rise in 'cloudified' devices. He was talking about the truth that increasingly more connected IoT devices are increasingly moving intelligence in the cloud towards the edge, with a continuum of compute capability between the cloud and the edge.
The theme of this year's embedded world 2022 conference in Nuremberg is a reflection of that trend: \”intelligent.connected.embedded\”. Conference sessions incorporate a see this theme from the cloud to edge continuum. For example, in the first edge computing session on first day of the conference features the following talks:
- Thomas Rosteck of Infineon Technologies addresses important aspects of innovation in the edge: more knowledge about trusted IoT systems, the convergence of hardware and software, the evolution of algorithms in AI, and upcoming new post-quantum cryptography algorithms because the answer to the challenges of quantum computers' world.
- Channa Samynathan of Amazon Web Services (AWS) talks about architecting embedded edge devices and scaling for complexity. He'll discuss best practices in design and manufacturing, provisioning, communication, ingestion, analytics, and application layers and just how an adequately designed edge architecture allows complex scaling scenarios.
- Chee Hoo Kok, a cloud software engineer at AIOBench, an Intel-funded startup venture, presents research around the effect of simultaneous multithreading (SMT) for the performance of cloud workloads; enabling or disabling SMT when hosting workload components will result in different workload throughputs. He presents categorizations of these based on the utilization patterns of CPU, memory, disk access and network traffic.
- George Grey of Foundries.io presents on migrating to cloud native solutions for embedded software development and deployment, including initiatives for free solutions, plus open architectures such as SOAFEE and SystemReady from Arm. Topics include cloud native and DevOps solutions for PKI-based device and fleet security infrastructure, OS solutions for different industry segments, tool and fleet deployment and orchestration, and ways of reducing costs of ultra-long term maintenance.
- Flavio Bonomi of Lynx Software Technologies delves further into driving the push in the cloud towards the edge. His paper discusses how implementing any functionality sensitive to issues in edge solutions represents an incorporation of requirements usual for embedded computing (security, real-time and safe, deterministic behaviors), into modern networked, virtualized, containerized lifecycle management and width=\”300\” height=\”300\”>
SECO demonstrates how you can add intelligence to edge devices using its Clea software platform which connects edge electronics using the cloud and facilitates real time device monitoring, analytics, infrastructure management, predictive maintenance, and secure remote software updates. Clea combines AI, IoT, and edge and cloud computing with customer-centric services and hardware solutions that can be off-the-shelf or tailor-made. The organization asserted with Clea, any device can be converted into a cloud-managed intelligent device, thus allowing smart control and monitoring, and gaining actionable, real-time insights using machine learning and artificial intelligence.
NXP Semiconductors will debut its new MCX portfolio of microcontrollers for smart homes, smart factories, smart cities and emerging industrial and IoT edge applications. It offers four number of devices built on a common platform and supported by the MCUXpresso suite of development tools and software. The portfolio features the very first instantiation of NXP's new, specialized neural processing unit (NPU) for accelerating inference in the edge, delivering up to 30x faster machine learning throughput over a CPU core alone. The portfolio is dependant on high-performance Arm Cortex-M cores, integrated having a group of peripherals and including up to 4 MB of on-chip flash memory, low power cache and advanced memory management controllers, plus up to 1MB of on-chip SRAM to help enhance real-time performance of edge applications.
At embedded computer level, Cincoze said it is making its debut at embedded world, with edge computing solutions for intelligent manufacturing in three areas: rugged embedded fanless computers, embedded GPU computers, and modular panel PC and industrial monitors. In ruggged computers, the DV-1000, launched in May, features a high-performance little design with wide temperature support (-40 -70°C), comes with an Intel Core i-series processor, DDR4 2666 MHz memory up to 128 GB, and has the most essential I/O ports for smart manufacturing.