Navigating the Tides – Overcoming Global Challenges in International Maritime Shipping with ZEDEDA

Event: Edge Field Day 3

Appearance: ZEDEDA Presents at Edge Field Day 3

Company: ZEDEDA

Video Links:

Personnel: Jason Grimm

In this presentation, ZEDEDA’s Consulting Solutions Architect, Jason Grimm, discusses how ZEDEDA’s edge platform is helping a global shipping company overcome significant challenges in monitoring and maintaining the integrity of refrigerated containers during long sea voyages. The shipping company operates over 600 vessels, transporting millions of containers, including up to a million refrigerated units carrying fresh and frozen goods. These containers must be kept at precise temperatures to avoid spoilage, which poses a $21 billion risk to the company. However, the lack of reliable connectivity on ships, which often rely on outdated radio networks, makes it difficult to monitor the containers in real-time, leading to potential spoilage and revenue loss.

ZEDEDA’s solution involves modernizing the shipping company’s communication infrastructure by upgrading from 2G to 4G and integrating Starlink satellite connectivity. This allows for real-time monitoring of IoT sensor data from the refrigerated containers, enabling the company to detect temperature fluctuations and take immediate action to prevent spoilage. ZEDEDA’s platform also ensures the security of the edge devices on the ships, preventing unauthorized access and tampering. By implementing a cloud-like operating model at the edge, ZEDEDA enables the shipping company to manage its fleet more efficiently, even without local IT staff on board, and to push updates and fixes remotely.

The presentation also highlights ZEDEDA’s broader capabilities, including the use of SD-WAN to manage communications between ships and shore, as well as the ability to orchestrate and automate various edge devices and applications. ZEDEDA’s platform not only reduces the risk of spoilage but also opens up opportunities for further improvements in operational safety and efficiency. The company plans to expand its solution to chartered vessels, offering a portable kit that can be deployed on leased ships. ZEDEDA’s edge platform is designed to be flexible and scalable, allowing customers to experiment with it using virtualized environments or inexpensive hardware like Raspberry Pi.


AI on Track – Revolutionizing Rail Freight and Solving the Industry’s Toughest Challenges with ZEDEDA

Event: Edge Field Day 3

Appearance: ZEDEDA Presents at Edge Field Day 3

Company: ZEDEDA

Video Links:

Personnel: Jason Grimm

New federal railway regulations have introduced the need for enhanced detection capabilities and more frequent inspections along railroad tracks. For large railway operators, this presents significant challenges, especially when managing thousands of miles of tracks in remote and harsh environments. ZEDEDA, through its unified edge platform, is helping one of the largest American railroad operators address these challenges. The operator, which manages 8,000 locomotives and transports over 500 million tons of freight annually, is required to increase the number of hotbox detectors from 4,000 to 20,000. These detectors monitor the temperature of train wheels to prevent derailments, a leading cause of accidents. The new regulations also mandate the use of advanced technologies like computer vision and AI inference at the edge to detect additional issues beyond just heat.

The scale of this deployment is a major technical challenge. Managing 20,000 edge devices in remote, often unsecured locations requires robust solutions for connectivity, security, and operational efficiency. ZEDEDA’s platform is designed to handle such scale, offering zero-touch deployment and management of edge devices. The platform ensures that devices are secure, even in physically vulnerable locations, by implementing measures like encryption, read-only file systems, and intrusion detection. Connectivity is another hurdle, as these devices must operate with various types of network connections, including 2G, 4G, satellite, and microwave. ZEDEDA’s platform simplifies this by automatically configuring devices based on available connectivity, ensuring seamless operation across diverse environments.

In addition to addressing current regulatory requirements, ZEDEDA’s platform provides flexibility for future advancements. The platform allows for the deployment of new applications and updates over time, enabling the railroad operator to adapt to evolving technologies and regulations. For example, future use cases include computer vision for wheel defect monitoring, railroad crossing safety, and advanced traffic control. By containerizing legacy systems and enabling real-time AI inference at the edge, ZEDEDA is helping the railroad industry modernize its operations, improve safety, and meet regulatory demands efficiently.


Driving Innovation – Empowering Service Centers for Software Dependent Vehicles with ZEDEDA

Event: Edge Field Day 3

Appearance: ZEDEDA Presents at Edge Field Day 3

Company: ZEDEDA

Video Links:

Personnel: Manny Calero

The presentation by Manny Calero from ZEDEDA at Edge Field Day 3 focused on how ZEDEDA is helping one of the world’s largest auto manufacturers modernize its dealership infrastructure to address the growing complexity of software-dependent vehicles. With the rise of electric vehicles (EVs) and increasingly stringent security regulations, such as UNECE R155, the need for secure and efficient software delivery has become paramount. The auto manufacturer, which produces around 8 million vehicles annually and services tens of millions more, faced challenges in delivering large, secure software updates to its 70,000 dealerships and service centers. ZEDEDA’s edge platform enables the manufacturer to securely manage and deliver these updates at scale, ensuring compliance with regulations and addressing bandwidth limitations.

ZEDEDA’s solution revolves around managing workloads at the edge, allowing dealerships to process software updates locally rather than transferring massive amounts of data from a central location. This approach not only reduces bandwidth usage but also ensures that each vehicle receives its unique software image securely. The platform is designed to handle large-scale deployments, managing tens of thousands of endpoints as a fleet, with a focus on security and flexibility. ZEDEDA’s platform is hardware-agnostic, supporting both x86 and ARM-based processors, which allows the manufacturer to avoid vendor lock-in and diversify its hardware supply chain, a lesson learned from the disruptions caused by the COVID-19 pandemic.

In addition to software updates, ZEDEDA’s platform is being used to consolidate other dealership applications, such as inventory management, onto a single edge platform. The platform’s flexibility allows it to support both legacy applications and modern cloud-native designs, such as Kubernetes. While the current focus is on dealership infrastructure, ZEDEDA is also working on other projects with the manufacturer, including in-car solutions and manufacturing use cases. The platform’s ability to manage edge devices at scale, with centralized cloud-based management, makes it a powerful tool for modernizing the infrastructure of industries like automotive, where secure and efficient software delivery is critical.


Edge Management and Orchestration with ZEDEDA

Event: Edge Field Day 3

Appearance: ZEDEDA Presents at Edge Field Day 3

Company: ZEDEDA

Video Links:

Personnel: Michael Maxey

ZEDEDA provides an edge orchestration solution that allows customers to manage their edge computing infrastructure with ease. The company was founded in 2016 with the vision of addressing the growing need for edge computing and the challenges of managing data at the edge. ZEDEDA’s platform enables customers to deploy and manage applications on any hardware at scale, while connecting to any cloud or on-premises systems. The company’s solution is particularly useful in industries such as oil and gas, agriculture, and manufacturing, where edge computing is critical for real-time data analysis, AI workloads, and secure software delivery. ZEDEDA’s open-source operating system, EVE (Edge Virtualization Engine), plays a central role in this, providing a lightweight, secure, and flexible platform for running various workloads, including virtual machines and containerized applications.

ZEDEDA’s platform is designed to address the unique challenges of edge environments, such as limited network connectivity, security risks, and the need for remote management. For example, in the oil and gas industry, ZEDEDA’s solution is used to deploy AI-powered analytics at well sites to optimize oil extraction and monitor methane burnoff using computer vision. The platform also supports a wide range of hardware, from small devices like Raspberry Pi to large Intel servers with GPUs, making it adaptable to different use cases. ZEDEDA’s customers include large enterprises like Chevron and Emerson, who not only use the platform but have also invested in the company, demonstrating the strategic importance of ZEDEDA’s technology in their operations.

The platform’s architecture is built around a cloud-based controller that manages edge devices through a secure API, ensuring that all configurations and updates are centrally managed. This eliminates the need for local access to edge devices, enhancing security and reducing the risk of tampering. ZEDEDA also emphasizes the importance of zero-touch updates and measured boot processes to ensure that devices remain secure and operational without requiring physical intervention. The platform supports a wide range of applications, from legacy systems to modern Kubernetes clusters, making it a versatile solution for edge computing across various industries.


Enabling AI at the Edge with Tsecond’s BRYCK AI Platform

Event: Edge Field Day 3

Appearance: Tsecond Introduces Edge AI at Edge Field Day 3

Company: Tsecond

Video Links:

Personnel: Manavalan Krishnan

Enterprises are increasingly facing challenges in processing large volumes of data at the edge, especially with the growing demand for AI-driven decision-making. Traditionally, edge data is sent to the cloud for processing, but this approach is becoming impractical due to network limitations and the sheer volume of data being generated. Tsecond’s BRYCK AI platform addresses these challenges by enabling AI inferencing directly at the edge, eliminating the need to transfer data to the cloud. The BRYCK AI platform integrates both storage and AI processing capabilities within a single unit, allowing for real-time decision-making without the need for external GPUs or cloud connectivity. This is particularly beneficial for edge environments where traditional AI infrastructure, such as GPUs, cannot be easily deployed due to space and power constraints.

The BRYCK AI platform is highly configurable, allowing customers to tailor the amount of storage and AI processing power to their specific needs. It supports a wide range of AI workloads, from small-scale applications like drones to large-scale operations such as ships or satellites. The platform’s architecture is designed for high-speed data processing, with both storage and AI chips connected via PCIe Gen 4, ensuring minimal latency and eliminating bottlenecks typically seen in traditional GPU-based systems. This allows for faster inferencing, with benchmarks showing the BRYCK AI platform to be 10 to 20 times faster than comparable Nvidia solutions. Additionally, the platform is power-efficient, making it suitable for deployment in various edge environments where power consumption is a critical factor.

Tsecond offers different configurations of the BRYCK AI platform, including rugged, portable versions for harsh environments and more flexible, open versions for data center use. The platform also supports a leasing model, allowing customers to upgrade their systems as their needs evolve. The BRYCK AI platform is designed to handle large-scale data processing, such as in manufacturing plants generating petabytes of data daily, where traditional AI systems would struggle to keep up. By integrating AI processing directly with storage, the BRYCK AI platform provides a scalable, efficient solution for edge AI inferencing, enabling enterprises to make real-time decisions without the delays and limitations of cloud-based processing.


Lights, Camera, Action – How Tsecond’s BRYCK Platform is Advancing Creative Workflows​

Event: Edge Field Day 3

Appearance: Tsecond Introduces Edge AI at Edge Field Day 3

Company: Tsecond

Video Links:

Personnel: Jimmy Fusil

In the presentation, Jimmy Fusil, a Media and Entertainment (M&E) technologist at Tsecond, discusses the challenges of managing vast amounts of data in the media and entertainment industry, particularly in film and TV production. He highlights how the transition from analog to digital workflows over the past 25 years has led to an explosion in data generation, with increasingly larger file sizes due to advancements in resolution from SD to 8K and beyond. This shift has made data the most valuable asset in production, requiring secure, efficient, and high-speed storage solutions. Tsecond’s BRYCK platform, a portable NVMe storage device, addresses these challenges by enabling on-device data processing, secure transport, and quick backups, making it an essential tool for modern creative workflows.

Fusil emphasizes that film production is inherently an edge-centric activity, where data is generated in real-time on set and needs to be processed and accessed immediately. Directors and cinematographers require low-latency, high-throughput solutions to review footage and make creative decisions on the spot. The BRYCK platform, with its ability to handle up to a petabyte of data and deliver high throughput, ensures that large volumes of data can be accessed and processed quickly, even in remote or challenging environments. Fusil also discusses the importance of portability, as BRYCK allows data to be easily transported from one location to another, such as from a film set to a post-production facility, without the need for lengthy data transfers.

Several use cases are presented to illustrate the BRYCK’s capabilities, including a miniseries production where 130 terabytes of data were transported from New York to Los Angeles for color grading, and a high-profile project for Sphere, which required handling 16K footage at 60 frames per second. Additionally, Fusil describes an extreme use case in the Atacama Desert, where BRYCK was used to record 150 terabytes of footage from multiple cameras in harsh conditions. These examples demonstrate how BRYCK not only meets the high-performance demands of modern media production but also provides a reliable and flexible solution for managing data in edge environments.


Deep Dive Into Tsecond’s BRYCK Platform – Low SWaP Dense Petabyte Scale and High Performance Storage

Event: Edge Field Day 3

Appearance: Tsecond Introduces Edge AI at Edge Field Day 3

Company: Tsecond

Video Links:

Personnel: Manavalan Krishnan

Tsecond’s BRYCK platform is designed to address the growing challenges of data management at the edge, particularly in environments that are not conducive to traditional data center infrastructure. These edge environments, such as autonomous vehicles, aerospace, and military operations, generate massive amounts of data, often in the range of hundreds of terabytes to petabytes, and require solutions that can handle harsh conditions like extreme temperatures, vibrations, and limited power availability. The BRYCK platform is a rugged, portable NVMe storage device that can store up to a petabyte of data and transfer it at speeds of up to 40GBps. This makes it ideal for capturing, processing, and moving large datasets from remote or mobile edges to data centers or the cloud, overcoming the limitations of network throughput and the time it takes to transfer data over traditional networks.

The BRYCK platform is built with a focus on security and durability, making it suitable for sensitive applications in sectors like government, defense, and aerospace. The device is designed to be tamper-proof, using a process called potting, which makes it nearly impossible to access the internal components without damaging the device. Additionally, the BRYCK is equipped with fault-tolerant mechanisms, such as erasure coding and self-healing capabilities, to protect data from various types of failures, including electrical, mechanical, and environmental damage. The platform also supports multi-level encryption and key management, ensuring that data remains secure even if the device is physically transported across borders or between different locations. This level of security and resilience makes the BRYCK a reliable solution for transporting sensitive data in military and aerospace applications.

In addition to its hardware capabilities, the BRYCK platform integrates with Tsecond’s software stack, which provides enterprise-grade storage features like high-speed data transfer, self-healing, and snapshot capabilities. The platform is also designed to work seamlessly with major cloud providers like AWS, Azure, and Google Cloud, enabling fast data uploads and downloads. Tsecond offers a service called DataDot, which facilitates high-speed data transfers to the cloud by physically moving BRYCK devices to data centers with high-speed cloud connections. This service is particularly useful for industries like media and entertainment, where large datasets need to be processed quickly, as well as for autonomous vehicle testing, where vast amounts of sensor data are generated daily. Overall, the BRYCK platform provides a comprehensive solution for managing, securing, and transporting large datasets in challenging edge environments.


Enabling Large Data Capture, Data Transport and AI Inferencing with Tsecond’s Edge Infrastructure​​

Event: Edge Field Day 3

Appearance: Tsecond Introduces Edge AI at Edge Field Day 3

Company: Tsecond

Video Links:

Personnel: Sahil Chawla

Edge environments often face challenges related to power, space, and connectivity, which can hinder the ability to collect, transport, and analyze large amounts of data. In this presentation, Sahil Chawla, co-founder and CEO of Tsecond, introduces the company’s innovations in edge infrastructure, focusing on secure data storage, seamless data movement, and rapid AI inferencing. Tsecond was founded in 2020 after identifying the need for modern solutions to handle the increasing data generated at the edge, particularly in industries like manufacturing, oil and gas, and autonomous vehicles. The company’s flagship product, the BRIC platform, is designed to address these challenges by offering a compact, high-capacity storage solution that can capture and store up to one petabyte of data in a small form factor, with deduplication capabilities that can increase storage efficiency by up to 8x.

Tsecond’s BRIC platform is built to be lightweight, power-efficient, and scalable, making it ideal for edge environments where space and power are limited. The system is capable of high throughput, with theoretical speeds of up to 256 GB per second, and is designed to be sustainable, consuming less power than a typical hair dryer. The BRIC platform can be customized with AI accelerator chips, allowing for on-site AI inferencing alongside data storage. This flexibility makes it suitable for a wide range of industries, from aerospace and aviation to media and entertainment, where large amounts of data need to be processed and analyzed quickly. Tsecond also offers a service called Data Dart, which facilitates the secure movement of data between edge locations and centralized data centers or the cloud.

The company’s innovations are inspired by existing solutions like Amazon Snowball and Seagate Live, but Tsecond aims to provide a more compact, efficient, and versatile alternative. Their products are designed to meet the specific needs of industries that require rugged, high-performance systems capable of handling large data volumes in remote or challenging environments. Tsecond’s focus on sustainability and security also positions them to address future demands for energy-efficient data centers and secure data transfer solutions. With a growing portfolio of products and a strong focus on edge AI and storage infrastructure, Tsecond is poised to play a significant role in the evolving landscape of edge computing.


Safeguarding On-Site Edge Applications and Data with Avassa

Event: Edge Field Day 3

Appearance: Avassa Presents at Edge Field Day 3

Company: Avassa

Video Links:

Personnel: Carl Moberg, Fredrik Jansson

In this demo, we explore best practices for handling sensitive data at the distributed edge and how to safeguard it against potential breaches with Avassa’s edge-native security features. We illustrate what happens when a host running business-critical applications is unexpectedly powered down or even stolen.

Discover how cryptographic materials stored in memory are immediately erased, locking down the secrets management vault and certain event streaming topics. By the end of this video, you’ll understand how Avassa protects sensitive data against security breaches across edge sites, ensuring resilient and secure deployments.

Learn more about security at the edge in this whitepaper: https://info.avassa.io/securing-the-edge


Managing Applications in Offline Edge Scenarios with Avassa

Event: Edge Field Day 3

Appearance: Avassa Presents at Edge Field Day 3

Company: Avassa

Video Links:

Personnel: Carl Moberg, Fredrik Jansson

Discover how Avassa ensures robust edge deployments with site-local clustering and offline capabilities. In this demo, we dive into the critical aspects of maintaining high availability for your containerized applications at the edge, even when connectivity is disrupted.

We cover the essential services needed for smooth operations—such as integrated logging and secrets management. Learn how Avassa simplifies log collection and analysis across distributed sites, ensuring quick access to operational insights. Our demo also highlights how secrets are securely managed, with encrypted storage and seamless distribution across edge sites.

By the end of this video, you’ll understand how Avassa’s site-local clustering and offline capabilities, combined with its logging and secrets management, provide a resilient and secure environment for your edge applications.

Book a demo of the Avassa Edge Platform: https://info.avassa.io/demo


How to Migrate Legacy VMs to Containers with Avassa

Event: Edge Field Day 3

Appearance: Avassa Presents at Edge Field Day 3

Company: Avassa

Video Links:

Personnel: Carl Moberg, Fredrik Jansson

Looking to transition away from a single VM to a containerized environment at the edge? In this demo, we show how Avassa makes it easy to migrate to distributed container applications at the edge. We start by outlining the limitations of VMs at the edge and introduce the core concepts: container package formats, distribution mechanisms, and lifecycle operations in the edge container runtime.

See how Avassa uses these to simplify edge deployments including packaging VMs as containers during a migration period. We deploy a sample application and demonstrate full health probe functionality and monitoring for real-time insights. Then, we seamlessly replace the VM-based application with a new Linux-native container version, showcasing a smooth upgrade process with minimal downtime. By the end, you’ll understand how Avassa empowers efficient application migration from VMs to containers at the edge.

Book a demo of the Avassa Edge Platform: https://info.avassa.io/demo


Transform Manufacturing with VMware Edge Compute Stack

Event: Edge Field Day 3

Appearance: VMware Presents at Edge Field Day 3

Company: VMware

Video Links:

Personnel: Chris Taylor

Smart Manufacturing, also known as Industry 4.0, has the potential to significantly improve operational technology (OT) environments by enhancing productivity and quality. VMware’s Edge Compute Stack is designed to optimize the deployment and management of these advanced technologies across factory settings. By leveraging real-time and deterministic processing of virtual Programmable Logic Controllers (PLCs) and other industrial applications, VMware enables manufacturers to achieve greater agility, enhanced security, and improved sustainability in their operations.

Traditionally, manufacturing environments rely on separate systems for different functions, such as human-machine interfaces (HMI), robot control, and PLCs for conveyor belts. VMware’s solution allows for the virtualization of these systems, enabling them to run concurrently on a single server. This approach not only simplifies the infrastructure but also allows for the integration of both real-time and non-real-time applications. For example, VMware has been working with Audi, which operates with thousands of industrial PCs across its factories, to virtualize these systems and optimize their performance based on latency requirements.

By virtualizing the factory floor, VMware’s Edge Compute Stack offers manufacturers the flexibility to deploy applications either on the factory line or in the server room, depending on their specific needs. This approach reduces the need for multiple physical systems, streamlines operations, and enhances the overall efficiency of the manufacturing process. The collaboration with Audi demonstrates the practical application of this technology, showcasing how edge computing can transform traditional manufacturing environments into more agile, secure, and sustainable operations.

Presented by Chris Taylor, Product Marketing, Software-Defined Edge

See how Audi is using edge compute in their factory: https://www.vmware.com/explore/video-library/video/6360760638112


VMware Edge Compute Stack Deep Dive and Demo

Event: Edge Field Day 3

Appearance: VMware Presents at Edge Field Day 3

Company: VMware

Video Links:

Personnel: Alan Renouf

Deploying applications at edge sites, close to where data is generated, presents unique challenges compared to traditional cloud or data center environments. Edge sites face limited connectivity or private network constraints, physical security constraints, and typically lack trained IT staff. In this session, discover how VMware simplifies fleet management of edge applications and infrastructure across thousands of resource-constrained sites using a GitOps and desired state management approach.

Deploying applications at edge sites presents unique challenges, such as limited connectivity, physical security constraints, and a lack of trained IT staff. VMware’s Edge Compute Stack aims to simplify the management of edge applications and infrastructure across thousands of resource-constrained sites using a GitOps and desired state management approach. GitOps allows infrastructure to be defined through text files, such as YAML, which specify the desired state of the infrastructure, including virtualization layers, networking configurations, and applications. These files are stored in a Git repository, providing version control, auditing, and security benefits. The infrastructure at the edge site can reference these files to configure itself, ensuring it remains in the desired state even when connectivity is limited.

VMware’s solution integrates both infrastructure and application management, bridging the gap between developers and infrastructure administrators. Developers can focus on creating applications, while infrastructure admins manage the platform on which these applications run. The platform supports continuous integration and continuous deployment (CICD) pipelines, allowing for automated testing and deployment of edge configurations. For example, a virtual edge device can be spun up in a CICD pipeline to test infrastructure and application changes before they are deployed to production. This approach ensures that updates are thoroughly tested and can be rolled out incrementally, starting with a few edge sites and gradually expanding to thousands.

The demo showcased how VMware’s Edge Compute Stack can be deployed on a small device, such as an Intel NUC, and how it integrates with GitOps to manage edge infrastructure. The demo included deploying a computer vision application and configuring a display screen connected to the edge device. The platform allows for easy deployment and management of applications, with the ability to monitor and update edge sites remotely. VMware’s solution is designed to provide a complete edge stack, including virtualization, Kubernetes, networking, and monitoring, making it a comprehensive solution for managing edge environments.

Presented by Alan Renouf, Technical Product Manager, Software-Defined Edge

Try VMware Edge Compute Stack: https://images.sw.broadcom.com/Web/CAInc2/{e35e3a44-c0c1-44aa-9da0-7ab729b5348d}_ECS_Trial_License_Request_091724.pdf


Enabling Mass Innovation with the VMware Edge Compute Stack

Event: Edge Field Day 3

Appearance: VMware Presents at Edge Field Day 3

Company: VMware

Video Links:

Personnel: Alan Renouf, Chris Taylor

Across all industries, organizations are adding intelligence to enhance their business operations at the edge for lower costs, higher quality, and increased sales. But scaling out innovation across sites is challenging. VMware’s approach removes edge complexity with zero touch operations.

VMware’s approach to edge computing focuses on simplifying operations and enabling mass innovation across industries by addressing the unique challenges of managing distributed infrastructure at scale. The VMware Edge Compute Stack is designed to handle the complexities of edge environments, where organizations are increasingly deploying intelligent systems to enhance business operations. These edge environments, such as retail stores, manufacturing plants, and energy substations, require localized computing power to process large amounts of data in real-time, often without reliable network connectivity. VMware’s solution integrates edge computing with networking and security services, offering a full-stack approach that includes SD-WAN and SASE technologies to ensure reliable and secure operations across dispersed locations.

The VMware Edge Compute Stack is built to handle the specific constraints of edge environments, such as limited on-site personnel, ruggedized hardware, and the need for real-time processing. The platform supports both virtual machines and containerized applications, allowing organizations to run legacy systems alongside modern applications. VMware’s orchestration platform, Edge Cloud Orchestra, enables zero-touch provisioning, making it easier to deploy and manage edge infrastructure without requiring IT staff at each location. This pull-based management model, inspired by the way smartphones update themselves, allows edge devices to autonomously check for updates and install them, reducing the need for manual intervention and minimizing downtime.

VMware’s edge computing solutions are already being used in various industries, including retail, manufacturing, and energy. For example, in retail, edge computing is used for loss prevention through computer vision, while in manufacturing, companies like Audi are using edge AI to improve the precision of welding robots and torque wrenches. In the energy sector, virtualizing electrical substations allows for faster response times and reduced operational costs. VMware’s flexible and scalable platform is designed to meet the evolving needs of edge environments, ensuring that organizations can innovate and optimize their operations while maintaining security and reliability.

Presented by Alan Renouf, Technical Product Manager, Software-Defined Edge and Chris Taylor, Product Marketing, Software-Defined Edge

See how VMware Edge Compute Stack works: https://www.youtube.com/watch?v=LiJ3YAWDASw


Less AI Chat More Action – AI Field Day 5 Delegate Roundtable Discussion

Event: AI Field Day 5

Appearance: AI Field Day 5 Delegate Roundtable Discussions

Company: Tech Field Day

Video Links:

Personnel: Alastair Cooke

The AI Field Day 5 delegate roundtable discussion, moderated by Alastair Cooke, centered on the prevalent use of chat-based interfaces in AI applications and the desire for more actionable AI solutions. The participants expressed frustration with the current trend of AI providing verbose responses to simple queries, arguing that AI should enhance applications rather than dominate them. They emphasized that AI should be a feature that improves the functionality of applications, rather than being the focal point. The discussion highlighted the need for AI to perform useful tasks, such as automating expense reports, rather than merely engaging in dialogue.

The delegates discussed the limitations of chat interfaces and the potential for AI to take more direct actions on behalf of users. They pointed out that while chatbots can be useful in certain scenarios, such as customer service, the ultimate goal should be for AI to perform tasks autonomously without requiring constant user input. The conversation also touched on the issue of trust in AI, noting that while users may not fully trust AI to take actions independently, they could still benefit from AI performing preliminary tasks that users can then review and approve. The participants agreed that AI should be used to handle repetitive and tedious tasks that humans are not well-suited for, thereby enhancing productivity and efficiency.

The roundtable concluded with a vision for the future of AI, where chat-based applications have their place, but are complemented by other forms of AI that can perform more complex and useful tasks. The delegates emphasized the importance of using the right AI tools for the right problems and moving beyond the current fascination with large language models and chat interfaces. They envisioned a future where AI is seamlessly integrated into applications, performing tasks that improve users’ lives without detracting from their experiences. The discussion underscored the need for AI to be a tool that assists and augments human capabilities, rather than replacing them or becoming a source of frustration.


AI Is Not Your Friend – AI Field Day 5 Delegate Roundtable Discussion

Event: AI Field Day 5

Appearance: AI Field Day 5 Delegate Roundtable Discussions

Company: Tech Field Day

Video Links:

Personnel: Stephen Foskett

The roundtable discussion at AI Field Day 5, moderated by Stephen Foskett, delved into the overly friendly nature of AI products and the implications of this design choice. The conversation began with the observation that many AI interfaces are designed to be exceedingly polite and user-friendly, akin to a vending machine thanking you after a frustrating interaction. While this friendliness is preferable to a rude AI, it can be misleading as it creates an illusion of companionship. The delegates shared their experiences with AI chat services, noting that while these systems are polite, they often fail to meet the user’s actual needs, leading to frustration. The discussion highlighted the need for AI to be efficient and effective rather than just friendly.

The conversation then shifted to the broader implications of AI and smart technology, particularly the pervasive data collection and surveillance. The delegates expressed concerns about the lack of user control over data collected by smart devices, such as TVs and cars, which often gather and transmit data without explicit user consent. This data is valuable to companies for targeted advertising and other purposes, raising significant privacy issues. The discussion underscored the tension between the benefits of smart technology, such as improved accessibility and convenience, and the invasive nature of data collection. The delegates argued that while AI and smart devices can enhance quality of life, especially for individuals with disabilities, the trade-off often involves sacrificing privacy and autonomy.

Finally, the roundtable touched on the regulatory landscape and the need for stronger protections against data misuse. The delegates noted that while some regions, like Europe, have more stringent privacy regulations, the enforcement and effectiveness of these laws vary. The conversation highlighted the role of regulation in ensuring that companies do not exploit user data and the importance of collective decision-making in addressing these issues. The discussion concluded with a reflection on the future of AI and smart technology, emphasizing the need for a balance between innovation and privacy, and the importance of designing AI systems that are both user-friendly and respectful of user autonomy.


Solving AI Cluster Scaling and Reliability Challenges in Training, Inference, RAG, and In-Memory Database Applications with Enfabrica

Event: AI Field Day 5

Appearance: Enfabrica Presents at AI Field Day 5

Company: Enfabrica

Video Links:

Personnel: Rochan Sankar

Enfabrica’s presentation at AI Field Day 5, led by founder and CEO Rochan Sankar, delved into the company’s innovative solutions for addressing AI cluster scaling and reliability challenges. Sankar highlighted the benefits of Enfabrica’s Aggregation and Collapsing Fabric System (ACFS), which enables wide fabrics with fewer hops, significantly reducing GPU-to-GPU hop latency. This reduction in latency is crucial for improving the performance of parallel workloads across GPUs, not just in training but also in other applications. The ACFS allows for a 32x multiplier in network ports, facilitating the connection of up to 500,000 GPUs in just two layers of switching, compared to the traditional three layers. This streamlined architecture enhances job performance and increases utilization, offering a potential 50-60% savings in total cost of ownership (TCO) on the network side.

Sankar also discussed the resiliency improvements brought by the multi-planar switch fabric, which ensures that every GPU or connected element can multipath out in case of failures. This hardware-based failover mechanism allows for immediate traffic rerouting without loss, while software optimizations ensure optimal load balancing. The presentation emphasized the importance of this resiliency, especially as AI clusters scale and the network’s reliability becomes increasingly critical. Enfabrica’s approach addresses the challenges posed by optical connections and high failure rates, ensuring that GPU operations remain unaffected by individual component failures, thus maintaining overall system performance and reliability.

In the context of AI inference and retrieval-augmented generation (RAG), Sankar explained how the ACFS can provide massive bandwidth to both accelerators and memory, creating a memory area network with microsecond access times. This architecture supports a tiered cache-driven approach, optimizing the use of expensive memory resources like HBM. By leveraging cheaper memory options and shared memory elements, Enfabrica’s solution can significantly enhance the efficiency and scalability of AI inference workloads. The presentation concluded with a summary of the ACFS’s capabilities, including high throughput, programmatic control of the fabric, and substantial power savings, positioning it as a critical component for next-generation data centers and large-scale AI deployments.


Enfabrica’s Approach to Solving IO Scaling Challenges in Accelerated Compute Clusters using Networking Silicon

Event: AI Field Day 5

Appearance: Enfabrica Presents at AI Field Day 5

Company: Enfabrica

Video Links:

Personnel: Rochan Sankar

Enfabrica, under the leadership of Rochan Sankar, has developed a novel solution to address the I/O scaling challenges in accelerated compute clusters by leveraging networking silicon. Their approach, termed the Accelerated Compute Fabric (ACF), refactors the traditional endpoint attachment to accelerators. Instead of using a single RDMA NIC for each accelerator, Enfabrica’s solution employs a fully connected I/O hub that integrates the functionalities of a PCI switch, an array of NICs, and a network switch into a single device. This ACF card connects to a scalable compute surface on one side and a scalable network surface on the other, facilitating high port density and efficient data movement.

The ACF architecture aims to eliminate inefficiencies in the current system where GPUs communicate through multiple layers of PCI switches and NICs to scale out. By collapsing these layers into a single, more efficient system, Enfabrica’s solution reduces the number of memory copies and improves burst bandwidth to GPUs, thereby enhancing overall compute efficiency. The ACF device supports both scale-up and scale-out interfaces, allowing it to handle memory reads and writes directly into memory spaces and communicate packets over long distances. This design is particularly beneficial for AI workloads, which require rapid and efficient data movement across large compute clusters.

Enfabrica’s ACF device is designed to be compatible with existing programming models and protocols, ensuring seamless integration into current data center architectures. The device supports standard PCIe and CXL interfaces, and its programmability allows for flexible transport and congestion control. By integrating multiple NICs and a crossbar switch within a single chip, the ACF device offers enhanced resiliency and load balancing capabilities. This innovative approach not only addresses the immediate scaling challenges faced by AI and accelerated computing workloads but also positions Enfabrica as a key player in the evolving landscape of data center architecture.


Accelerated Compute for AI from a Systems Perspective with Enfabrica

Event: AI Field Day 5

Appearance: Enfabrica Presents at AI Field Day 5

Company: Enfabrica

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Personnel: Rochan Sankar

Enfabrica, led by founder and CEO Rochan Sankar, is pioneering a new category of networking silicon designed to support accelerated computing and AI at unprecedented scales. The company has developed the Accelerated Compute Fabric Supernick (ACFS), a product aimed at addressing the evolving needs of data centers as they increasingly incorporate GPUs and TPUs. Sankar highlights that traditional networking solutions are no longer sufficient due to the rapid increase in compute intensity, which has outpaced the growth of I/O and memory bandwidth. This imbalance creates significant challenges for building distributed, resilient, and scalable systems, necessitating a rethinking of system I/O architectures.

The ACFS, specifically designed for high-performance distributed AI and GPU server networking, represents a significant leap in networking capabilities. Enfabrica’s first chip, codenamed Millennium, achieves an unprecedented 8 terabits per second of bandwidth, compared to the current standard of 400 gigabits per second. This innovation addresses the critical issue of compute flops scaling faster than data movement capabilities, which has led to inefficiencies in model performance and hardware utilization. Sankar explains that the current system architectures, which were originally designed for traditional compute, are not optimized for the demands of modern AI workloads, leading to inefficiencies and bottlenecks.

Sankar also discusses the historical context of computing models, contrasting the tightly coupled, low-latency communication of supercomputers with the distributed, high-tolerance networking of hyperscale cloud systems. Modern AI and machine learning systems require a hybrid approach that combines the performance of supercomputers with the scalability and resilience of cloud infrastructure. However, current solutions involve disparate communication networks that do not effectively interoperate, leading to imbalanced bandwidth and inefficiencies. Enfabrica aims to address these challenges by creating a unified networking fabric that can support both tightly coupled and distributed computing models, thereby improving overall system efficiency and scalability.


AI Networking Visibility with Arista

Event: AI Field Day 5

Appearance: Arista Presents at AI Field Day 5

Company: Arista

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Personnel: Tom Emmons

In the presentation at AI Field Day 5, Tom Emmons, the Software Engineering Lead for AI Networking at Arista Networks, discussed the challenges and solutions related to AI networking visibility. Traditional network monitoring strategies, which rely on interface counters and packet drops, are insufficient for AI networks due to the high-speed interactions that occur at microsecond and millisecond intervals. To address this, Arista has developed advanced telemetry tools to provide more granular insights into network performance. One such tool is the AI Analyzer, which captures traffic statistics at 100-microsecond intervals, allowing for a detailed view of network behavior that traditional second-scale counters miss. This tool helps identify issues like congestion and load balancing inefficiencies by providing a microsecond-level perspective on network traffic.

Emmons also introduced the AI Agent, an extension of Arista’s EOS (Extensible Operating System) to the NIC (Network Interface Card) servers. This feature allows for centralized management and monitoring of both the Top of Rack (TOR) switches and the NIC connections. The AI Agent facilitates auto-discovery and configuration synchronization between the switch and the NIC, ensuring consistent network settings across the entire infrastructure. This centralized approach helps prevent common issues such as mismatched configurations between network devices and servers, which can lead to suboptimal performance. The AI Agent’s ability to integrate with various NICs through specific plugins further enhances its versatility and applicability in diverse network environments.

Additionally, the AI Agent’s integration with Arista’s CloudVision software provides a unified management view that includes both network and server statistics. This comprehensive visibility enables network engineers to correlate network events with server-side issues, significantly improving the efficiency of network troubleshooting. By incorporating AI and machine learning techniques, Arista aims to identify real anomalies and correlate them with network events, thereby distinguishing between genuine issues and noise. This holistic approach to network visibility and debugging ensures that engineers can quickly and accurately diagnose and resolve performance problems, ultimately leading to more reliable and efficient AI network operations.


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