Why Every OEM Needs an Edge Strategy: Insights from Arrow Intelligent Solutions

The landscape of data processing is undergoing a massive shift. As artificial intelligence (AI) and particularly Generative (Gen) AI continue the relentless march forward, original equipment manufacturers (OEMs) face a critical question: where will the data be processed and stored? Let’s delve into the evolving world of far edge, near edge, and on-premise computing, exploring the considerations for OEMs seeking to build Gen AI solutions. Gen AI, while powerful at the edge, introduces some unique challenges to edge infrastructure, including: Massive datasets: Gen AI algorithms often require vast amounts of training data, which can strain far edge and near edge resources. Limited processing power: Gen AI models are computationally intensive. Edge devices, often with limited processing power compared to on-premise servers, can struggle to run complex Gen AI models efficiently. Real-time or near real-time needs: Certain Gen AI applications, like personalized recommendations or anomaly detection, might necessitate real-time or near real-time processing, pushing the boundaries of latency tolerance. The traditional centralized data processing model in the cloud is giving way to a more distributed approach. Leveraging a combination of far edge, near edge, and cloud resources can distribute the processing load. Far edge devices can perform initial data collection and pre-processing, while near edge nodes handle preliminary training and filtering. Complex Gen AI computations can then be offloaded to on-premise data centers or the cloud. Figure 1: Distributed computing: From the Far Edge to the Core/Cloud Far Edge Computing: Extending the Boundaries Far edge computing refers to processing data at the outermost edge of the network, close to where data is generated, which is particularly beneficial in scenarios where low latency and real-time processing are crucial. Applications in Gen AI Advantages of Far-Edge Processing Smart Surveillance and Security: Gen AI models at the far edge can analyze video feeds in real time to detect threats and recognize faces, reducing the need for data transmission to central servers. Autonomous Robots or Vehicles Robots and vehicles equipped with edge devices can instantaneously process sensor and camera data, enabling safer navigation and obstacle avoidance. Reduced Latency: 5 to 20 ms. Immediate data processing near the source. Bandwidth Efficiency: Less data goes to centralized cloud systems. Enhanced Privacy: Local data processing mitigates the risk of data breaches.   Near Edge Computing: Bridging the Gap Acting as a bridge between far edge devices and the cloud, near edge devices offer more processing power than their far edge counterparts. Gateways, local servers, and fog computing nodes typically reside at the near edge, handling tasks like data aggregation, filtering, and preliminary analytics. Applications in Gen AI Advantages of Near-Edge Processing Retail: Near edge devices can analyze customer behavior in real time to provide personalized shopping experiences and manage inventory efficiently. Smart Cities: Environmental monitoring systems at the near edge can swiftly process and respond to data on air quality, noise levels, and traffic patterns. Balanced Latency: Up to 50 ms. Quicker than centralized processing, suitable for applications that require timely responses but are not instantaneous. Scalability: Easier to scale across multiple locations compared to far edge devices. Cost-Effective: Reduces the need for extensive infrastructure at the far edge.   On-Premise/Cloud Computing: Control and Customization On-premise computing involves deploying and managing computing resources within the organization’s physical location. This approach offers high levels of control and security but can be less scalable than cloud-based solutions. Applications in Gen AI Advantages of On-Premise/Cloud Processing Healthcare: Hospitals can use on-premise Gen AI systems to analyze medical imaging and patient data, ensuring compliance with stringent data privacy regulations. Industrial IoT (IIoT): Manufacturing plants can deploy AI models to monitor machinery and predict maintenance needs, reducing downtime and improving productivity. Complete Control: Full data and infrastructure ownership allows for tailored solutions. Enhanced Security: Data remains within the organization's premises, reducing exposure to external threats. Customization: Flexibility to customize hardware and software to specific requirements.   6 Considerations for OEMs: Selecting the Right Infrastructure The choice between far edge, near edge, and on-premise processing depends on several factors for OEMs: Data volume and complexity: For applications generating large volumes of complex data, near edge or on-premise solutions might be necessary. Conversely, low-volume sensor data might be well-suited for far edge processing. Latency requirements: Real-time decision-making often necessitates on-site processing. Applications requiring minimal delay might favor near edge or on-premise solutions over the cloud. Security and privacy: Far or near edge processing can offer greater control and security for sensitive data. Regulatory compliance might also dictate processing location. Cost and scalability: Cloud-based solutions provide scalability and eliminate the need for upfront hardware investment. However, on-premise solutions can provide greater cost control over time. Power availability: Far edge devices often operate on limited power, necessitating efficient processing with minimal resource consumption. Operating environments: Temperature variations, dust, noise, and humidity are factors when determining where to deploy the infrastructure. Summary: The Future is Distributed The future of data processing lies in a distributed approach, with far edge, near edge, and on-premise solutions working in concert with the cloud. As Gen AI evolves, OEMs must adopt flexible and scalable processing strategies. By understanding the strengths and limitations of each processing platform and implementing a hybrid approach when necessary, OEMs can ensure their solutions effectively leverage the power of AI at the edge. Companies looking to leverage Gen AI at the edge can benefit from the combined expertise of Arrow and Dell OEM Solutions. Dell's robust portfolio provides the ideal building blocks for far edge, near edge, and on-premise deployments. Arrow can guide companies through the process, from selecting the optimal hardware for their Gen AI application to helping ensure a smooth supply chain and offering implementation support. Their expertise in solution orchestration enables companies to deploy and manage their Gen AI solutions at the edge with greater efficiency and reduced risk. Related Resources: eBook: Infrastructure Considerations for Rugged Edge Applications Product brief: Storage Portfolio At-a-Glance Infographic    

AI adoption permeates all industries, revolutionizing traditional processes, unlocking unprecedented use case applications, and opening new, unimaginable possibilities. According to GlobalData's forecast, the overall AI market will be worth $909B by 2030. The healthcare industry is in a transformative era with the adoption of AI technology. From pharmaceutical research and development to remote patient monitoring and in-home care, we are witnessing unprecedented leaps in robotic surgical procedures, precision imaging, and early disease detection, diagnosis, and treatment. In healthcare, AI innovation often happens at the edge, where data is created and handled close to where it occurs. The amount of data and the specific needs of medical applications require accurate and quick processing with minimal latency. The sharing and size of the data, as well as the quick response of edge medical devices, show how important it is to have a strong edge infrastructure. This requires an update or the creation of new designs to handle the power, capacity, and size needed to run many of these new AI-powered medical devices. The increasing rack densities and power requirements of high-performing chips and the higher heat loads from boosted compute power and super-sized servers highlight the critical need for efficient and rapid cooling. Modern imaging equipment like MRI machines, CT scanners, and X-ray devices demand extensive storage, high-speed processing capabilities, advanced computing, and operating systems. However, this doesn't necessarily mandate immediate radical overhauls. Initially, placing compact server units adjacent to machines near the source can support AI medical devices. Beyond the device build and embedded components, server support installs can go directly into the room. For larger installations and to support multiple devices, on-premise rack systems, micro data centers, or backup power sources such as UPS, complete with cooling and monitoring, can be installed in dedicated server room closets. Additional considerations include the number of devices, data volume, and real-time responses. One thing is certain: downtime is not an option. Arrow helps companies navigate the complexities of AI deployment. We work closely with leading technology and infrastructure providers such as Supermicro, APC, and Microsoft to develop certified, high-performing AI-ready devices, from board-level compute and operation systems to software, servers, and micro data centers. Whether you need to modify, expand, or design new architecture for your existing infrastructure, our integrated solutions help pave the way for a successful edge AI deployment in healthcare. Learn more about the impact of edge AI in healthcare in our brochure or speak to one of our experts for more guidance toward a seamless AI deployment. 

New technologies, including artificial intelligence (AI), edge computing, and digital twins, can help designers simulate the design and performance of new products without building them, saving critical time on prototyping and time to market. In the wake of the COVID-19 pandemic, the global landscape of healthcare has experienced a paradigm shift, with innovation becoming a driving force across various domains. One notable area witnessing substantial growth is the portable and remote patient monitoring market, projected to reach nearly $43 billion worldwide by 2027. The integration of AI and the Internet of Things (IoT) is at the forefront of this transformative wave, ushering in a new era of data-driven healthcare. Medical device manufacturers use these technologies to innovate their products to assist healthcare providers and improve patient care. The role of AI in transforming various sectors Beyond its traditional applications, AI spearheads a paradigm shift in medical device design. Integrating AI, edge computing, and digital twins has revolutionized the approach to creating medical devices. Designers can now simulate products and their performance without needing physical prototypes, saving crucial time in prototyping and expediting time-to-market. AI's Power in Discovering Patterns At the core of AI’s transformative impact lies its unparalleled ability to discern hidden correlations and patterns within vast datasets. This ability has led to a revolution in diagnostics, treatment plans, and patient care in healthcare. By efficiently identifying patterns, AI facilitates the creation of algorithms that can enhance medical professionals’ decision-making processes. Utilizing IoT, Sensors, and Analytics for Enhanced Efficiency The synergy of AI with IoT amplifies the capabilities of medical devices. IoT, essentially a network of connected devices, extends its reach to healthcare, keeping patients connected to care providers beyond traditional healthcare settings. Continuous monitoring, facilitated by sensors and IoT, is instrumental in managing chronic conditions such as diabetes and chronic obstructive pulmonary disease (COPD), improving care delivery, and enhancing patient quality of life. Application in Medical Devices Affordable, Mobile, and Continuously Improving In the domain of medical device design, AI brings forth a myriad of advantages. Affordability, mobility, and continuous improvement are vital facets reshaping the landscape. AI-driven medical devices are integral to modern healthcare systems, from constant blood glucose monitoring to transmitting vital patient data from ambulances to hospitals. Machine learning algorithms analyze vast datasets, incorporating variables such as material properties, usage patterns, and environmental factors, and enable designers to predict how a device will perform under different conditions, leading to more robust and efficient designs. AI fosters collaboration and knowledge sharing among stakeholders in the electronic medical device ecosystem. Designers, engineers, and healthcare professionals can leverage AI platforms to share insights, best practices, and real-world data. This collaborative approach accelerates the development cycle by tapping into a collective pool of knowledge, ultimately leading to more refined and effective medical devices. Critical Considerations in AI-Driven Medical Device Design While the potential benefits of AI in medical devices are immense, it is essential to recognize and address several crucial considerations. The complexity of integrating AI, data quality, adherence to standards, and the potential risks to patient safety are paramount concerns. Striking a balance between innovation and adherence to regulatory standards is critical to helping ensure the effectiveness and safety of AI-powered medical devices. Additionally, AI facilitates predictive maintenance for existing equipment. By analyzing sensor data and monitoring equipment performance, AI can predict when maintenance is required, minimize downtime, and ensure the reliability of the devices. Deployment Options for AI Algorithms in Medical Devices The deployment of AI algorithms in medical devices demands careful consideration. Whether integrated into diagnostic tools, treatment planning systems, or wearable devices, the seamless incorporation of AI requires a thorough understanding of the device’s purpose, the complexity of the algorithm, and the data it processes. Balancing efficiency with reliability is pivotal in the successful integration of AI into the medical device landscape. Furthermore, regulators such as the U.S. Food and Drug Administration (FDA) already issued an action plan for integrating AI and machine learning in medical devices. The FDA anticipates that many artificial intelligence and machine learning-driven software changes to a device may need a premarket review.  "The FDA would expect a commitment from manufacturers on transparency and real-world performance monitoring for artificial intelligence and machine learning-based software as a medical device, as well as periodic updates to the FDA on what changes were implemented as part of the approved pre-specifications and the algorithm change protocol." How Arrow Electronics Can Help Pave the Way for AI in Medical Devices The drive to enable healthcare excellence through AI innovation is at the core of Arrow's mission. By providing solutions that bridge the gap between cutting-edge technology and practical application, Arrow empowers the healthcare device makers and the industry to deliver accessible, efficient, and leading-edge products. As the healthcare industry undergoes this transformative journey, Arrow helps facilitate the seamless integration of AI in medical devices. Arrow’s Intelligent Solutions business addresses the challenges associated with AI implementation, assisting regulatory compliance, data security, and optimal performance. Environmental Sustainability Considerations Arrow aligns with evolving standards and regulations, emphasizing the importance of sustainability in material and manufacturing decisions. This commitment addresses regulatory requirements and reflects a broader industry shift towards environmentally conscious practices. Arrow is crucial in helping manufacturers make choices that contribute to a sustainable future. Arrow’s, device manufacturers’, and healthcare professionals’ collaborative efforts contribute to a future in which new medical devices are central in elevating patient outcomes. Conclusion Integrating AI in medical devices heralds a new era of healthcare innovation. The synergy of AI with IoT, sensors, and analytics is reshaping patient care, diagnostics, and treatment planning. As the medical device landscape evolves, considerations for complexity, data quality, standards adherence, and patient safety become increasingly crucial. Arrow provides solutions that pave the way for a future where AI-designed medical devices contribute to more accessible, efficient, and personalized healthcare. The journey towards the future of healthcare is now. Contact us to learn how Arrow can help guide your AI product transformation.  

Finalizing a new product can often be more complex than developing the initial prototype. While a prototype only needs to be functional, a product ready for the market must consider component lifecycle, cost, and supply chain management factors. In the medical equipment industry, optimizing the bill of materials (BOM) for a device’s computing aspect is crucial; this helps ensure cost-effectiveness while maintaining the desired quality and performance of the end product.