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Home > Resources > Whitepapers > Generative AI and Stable Diffusion Image Generation on the Dell PowerEdge XE9680 Server
June 29, 2023
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We’ve created a new category between low-code and custom software to provide flexible, bespoke apps at the speed and cost of an off-the-shelf product.
whitePaper | December 6, 2022
The impact of system downtimes is rapidly rising due to businesses’ increased dependency on data and technology. It can cause loss of opportunities, productivity loss, confidence erosion, SLA penalties, supply chain ripple effects, and most importantly, a damaged reputation.
whitePaper | September 28, 2022
As stated in the American Planning Association’s 2022 Trend Report for Planners, over centuries, professions have evolved with changing environments, shifting eras, new challenges, societal shifts, and technological innovations. While some professions have become obsolete or or were replaced by technology, others have adapted or readjusted and stayed relevant in an ever-changing world. What is different today is the pace of change, which requires faster, even proactive, adjustments—including new tools, processes, and skills.
whitePaper | September 29, 2021
Penguin Computing’s Analytics Practice acts as a single point of reference for hardware, software, architectural design, and much more, enabling organizations to focus their energy on their AI projects. Access this white paper to learn more about this secure, end-to-end solution designed to alleviate AI infrastructure woes.
whitePaper | May 31, 2022
End-to-End (E2E) testing has traditionally been seen as a ‘final frontier’ of sorts in software development.
whitePaper | September 20, 2022
Artificial intelligence and machine learning (AI/ML) is on every organization’s radar. Businesses and IT professionals are finding some amazing use cases for AI/ML and already experiencing its benefits, but there’s still so much untapped and unrealized potential.
whitePaper | January 20, 2022
Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing neural network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. To enable that, we present CMSIS-NN, an open-source library of optimized software kernels that maximize the NN performance on Cortex-M cores with minimal memory footprint overhead. We further present methods for NN architecture exploration, using image classification on CIFAR-10 dataset as an example, to develop models that fit on such constrained devices.
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