Radio-interferometric (RI) imaging entails solving high-resolution high-dynamic range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denoisers, such as AIRI. Optimization and PnP structures are however highly iterative, which hinders their ability to handle the extreme data sizes expected from future instruments. To address this scalability challenge, we introduce a novel deep learning approach, dubbed "Residual-to-Residual DNN series for high-Dynamic range imaging". R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of Deep Neural Networks (DNNs) taking the previous iteration's image estimate and associated data residual as inputs. It thus takes a hybrid structure between a PnP algorithm and a learned version of the matching pursuit algorithm that underpins CLEAN. We present a comprehensive study of our approach, featuring its multiple incarnations distinguished by their DNN architectures. We provide a detailed description of its training process, targeting a telescope-specific approach. R2D2's capability to deliver high precision is demonstrated in simulation, across a variety of image and observation settings using the Very Large Array (VLA). Its reconstruction speed is also demonstrated: with only few iterations required to clean data residuals at dynamic ranges up to 100000, R2D2 opens the door to fast precision imaging. R2D2 codes are available in the BASPLib library on GitHub.

Blood tests tell doctors about the function of key organs, and can reveal countless medical conditions, including heart disease, anemia and cancer. At major hospitals, the number of blood cell images awaiting analysis can be overwhelming. With over 10,000 beds and more than 8 million outpatient visits annually, Taiwan's Chang Gung Memorial Hospital collects at least a million blood cell images each year. Its clinicians must be on hand 24/7, since blood analysis is key in the emergency department. To improve its efficiency and accuracy, the health care network -- with seven hospitals across the island -- is adopting deep learning tools developed on AI-Ready Infrastructure, or AIRI.

Pure Storage, has announced a host of new and improved AI solutions that provide enterprise customers with the features and functionality needed to execute increasingly complex AI initiatives through any phase or scale.Built on Pure's industry-leading file and object system, FlashBlade, and its joint AI-Ready Infrastructure (AIRI) offering with NVIDIA, customers can develop and deploy AI rapidly to keep pace with modern business. "Enterprise organizations that have existed and done business one way for decades now find themselves working hard to build a business for the future. To truly compete going forward will require large-scale, multi-phase AI initiatives, and Pure has innovated with that particular set of challenges in mind, said Amy Fowler, VP of Strategy and Solutions for FlashBlade at Pure Storage. Organizations today are stuck with a siloed, traditional analytics infrastructure. AI Data Hub extends traditional analytics and provides more performance and security at a lower cost.

Pure Accelerate 2019 comes at a pivotal time in Pure Storage's evolution from a flash storage pioneer to a publicly traded company with more than $1 billion in annual revenue. Flash vendors are grappling with uneven prices for NAND flash -- a scenario some analysts predict will linger for at least the next several years. Pure Storage all-flash storage is not out of the headwinds, although it appears to be weathering the storm for now. After back-to-back quarters of stalled flash sales, Pure all-flash revenue jumped last quarter. The company signed up more than 450 new enterprises, bringing its total customer base to around 6,600 paying customers.

Roy Kim leads FlashBlade products and solutions team at Pure Storage. Previously, Roy spent eight years at NVIDIA, leading product management and marketing efforts focused on artificial intelligence and analytics, helping drive a start-up within the company to a multi-billion dollar business. Roy has Masters of Computer Science and Bachelors of Science from M.I.T. IDG recently sat down with Roy Kim, an artificial intelligence and deep learning expert at Pure Storage, to discuss the data and storage needs of AI systems. Data is the fuel for AI, and as it turns out, AI challenges data storage systems like no other application ever has. There are generally two stages of AI processes, training and production.

Channelnomics catches up with Pure Storage to talk about its success and what the channel means to the company. Pure has always been committed to a 100 percent channel business model and we work closely with a select group of partners who lead their businesses with Pure. This model has resulted in accelerated growth and increased profitability for our partners and for our business. This year we launched a new Pure Partner Program, which includes strategic investments in the tools and resources needed to achieve a partner program that attracts, enables and retains top partners. The program includes new training, certifications, support and incentives, and rewards partners who build a Pure practice and lead with Pure in the market.

NetApp and Nvidia have introduced a combined AI reference architecture system to rival the Pure Storage-Nvidia AIRI system. It is aimed at deep learning and, unlike FlexPod (Cisco and NetApp's converge infrastructure), has no brand name. Unlike AIRI, neither does it have its own enclosure. A NetApp and Nvidia technical whitepaper – Scalable AI Infrastructure Designing For Real-World Deep Learning Use Cases (PDF) – defines a reference architecture (RA) for a NetApp A800 all-flash storage array and Nvidia DGX-1 GPU server system. There is a slower and less expensive A700 array-based RA.

Pure Storage and Nvidia have produced a converged machine-learning system to train AI models using millions of data points. It's called AIRI – AI-Ready Infrastructure – and combines a Pure FlashBlade all-flash array with four Nvidia DGX-1 GPU-accelerated boxes and a pair of 100GbitE switches from Arista. The system has been designed by Pure and Nvidia, and is said to be easier and simpler to buy, deploy, and operate than buying and integrating the components separately; the standard converged infrastructure pitch. AIRI's rack is meant to be an object of desire in your data centre. FlashBlade is Pure Storage's all-solid-state-storage array for fast access to unstructured data.