Secure Code Warrior | June 30, 2022
Secure Code Warrior, the global developer-driven security leader, was recognized as one of four “Cool Vendors in Software Engineering: Enhancing Developer Productivity”, by GartnerⓇ. Secure Code Warrior is the only company on the list who focuses on developer-driven security enabled by an intuitive learning platform.
According to Gartner, the definition of a Cool Vendor is “a small company offering a technology or service that is innovative, impactful or intriguing." Gartner’s report notes that “Software engineers struggle to navigate complex code environments and to improve security of the systems they build while remaining productive. These Cool Vendors offer innovative solutions that help software engineering leaders boost developer productivity and mitigate security risks.”
Engineers’ time constraints are taking away focus from security and was recognized as a significant challenge by Gartner, which was also apparent from Secure Code Warrior’s recent survey, ‘The State of Developer-Driven Security’. The report revealed that 76% of respondents also agreed that good training in secure coding would actually improve productivity with fewer security tickets and rework to manage.
“At Secure Code Warrior, our solutions are driven by the need to ensure software security, while improving developer productivity. “When armed with the right security skills and contextual tools, development teams can easily mitigate risk and consistently ship secure code at speed, without compromising innovation. We are honored to be included in the new Gartner Cool Vendors in Software Engineering report, and see this recognition as a testament to our strategy and future business direction to enable developer-driven security.”
Pieter Danhieux, Co-founder and CEO of Secure Code Warrior
Contact us to discover how Secure Code Warrior can enable developer-driven security through our flagship learning platform and embed security skills into the developer workflow with world-class integrations.
Gartner Disclaimer Gartner, Cool Vendors in Software Engineering: Enhancing Developer Productivity, 16 May 2022, Arun Batchu, Marty Resnick, Manjunath Bhat
Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
GARTNER and Cool Vendors are registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
About Secure Code Warrior
Secure Code Warrior builds a culture of security-driven developers by giving them the skills to code securely. Our flagship Learning Platform delivers relevant skills-based pathways, hands-on missions, and contextual tools for developers to rapidly learn, build, and apply their skills to write secure code at speed. Established in 2015, Secure Code Warrior has become a critical component for over 450 enterprises including leading financial services, retail, and global technology companies across the world.
DataRobot | February 14, 2022
AI Cloud leader DataRobot today announced its selection as an AI partner under a five-year $249 million-ceiling Blanket Purchase Agreement (BPA) awarded by the U.S. Department of Defense (DoD) Joint Artificial Intelligence Center (JAIC). Through this contract, DataRobot is tasked with transforming AI throughout the DoD ecosystem by providing its AI Cloud platform and services to accelerate the government’s use of emerging AI technologies including Machine Learning (ML), Deep Learning (DL), and Neural Networks (NN).
DataRobot will support JAIC’s commitment to strategic and world-class AI solutions by:
Providing a unified AI platform to enable all users across the DoD to quickly and successfully build and deploy mission-enabling AI projects
Detecting, measuring, and standardizing bias prevention as a routine step in the machine learning process, driving more reliable operations and strategic solutions
Operationalizing solutions for Testing and Evaluation (T&E) of AI-enabled systems, automated products, and autonomous systems
Integrating AI T&E tools and services in alignment with JAIC’s architectures, technical standards, and security standards
DataRobot’s end-to-end AI Cloud platform brings together disparate data and users, spanning expert data scientists to IT operators to analysts, through enhanced collaboration and continuous optimization across the entire AI lifecycle. Built as a multi-cloud platform, DataRobot AI Cloud can be deployed in a combination of public clouds, data centers, or at the edge, with governance to protect and secure even the most highly-regulated organizations.
AI has the power to shape the next generation of U.S. Defense operations, ensuring the safety of our citizens, personnel and allies. We’re proud to support the JAIC’s mission to maximize the full potential of AI, and we share the DoD’s commitment to solving complex and mission-critical problems with better, faster, data-driven solutions that are accessible for all.”
Jim Watson, VP, Sales, Federal & Public Sector, DataRobot
DataRobot AI Cloud is the next generation of AI. DataRobot’s AI Cloud vision is to bring together all data types, all users, and all environments to deliver critical business insights for every organization. DataRobot is trusted by global customers across industries and verticals, including a third of the Fortune 50
SK hynix | February 17, 2022
SK hynix or "the Company" announced on February 16 that it has developed PIM*, a next-generation memory chip with computing capabilities.
It has been generally accepted that memory chips store data and CPU or GPU, like human brain, process data. SK hynix, following its challenge to such notion and efforts to pursue innovation in the next-generation smart memory, has found a breakthrough solution with the development of the latest technology.
SK hynix plans to showcase its PIM development at the world's most prestigious semiconductor conference, 2022 ISSCC*, in San Francisco at the end of this month. The company expects continued efforts for innovation of this technology to bring the memory-centric computing, in which semiconductor memory plays a central role, a step closer to the reality in devices such as smartphones.
For the first product that adopts the PIM technology, SK hynix has developed a sample of GDDR6-AiM (Accelerator* in memory). The GDDR6-AiM adds computational functions to GDDR6* memory chips, which process data at 16Gbps. A combination of GDDR6-AiM with CPU or GPU instead of a typical DRAM makes certain computation speed 16 times faster. GDDR6-AiM is widely expected to be adopted for machine learning, high-performance computing, and big data computation and storage.
GDDR6-AiM runs on 1.25V, lower than the existing product's operating voltage of 1.35V. In addition, the PIM reduces data movement to the CPU and GPU, reducing power consumption by 80%. This, accordingly, helps SK hynix meet its commitment to ESG management by reducing carbon emissions of the devices that adopt this product.
SK hynix also plans to introduce a technology that combines GDDR6-AiM with AI chips in collaboration with SAPEON Inc., an AI chip company that recently spun off from SK Telecom.
The use of artificial neural network data has increased rapidly recently, requiring computing technology optimized for computational characteristics. We aim to maximize efficiency in data calculation, costs, and energy use by combining technologies from the two companies."
Ryu Soo-jung, CEO of SAPEON Inc.
Ahn Hyun, Head of Solution Development who spearheaded the development of the latest technology and product, said that "SK hynix will build a new memory solution ecosystem using GDDR6-AiM, which has its own computing function." He added that "the company will continue to evolve its business model and the direction for technology development."
About SK hynix Inc.
SK hynix Inc., headquartered in Korea, is the world's top tier semiconductor supplier offering Dynamic Random Access Memory chips ("DRAM"), flash memory chips ("NAND flash") and CMOS Image Sensors ("CIS") for a wide range of distinguished customers globally. The Company's shares are traded on the Korea Exchange, and the Global Depository shares are listed on the Luxemburg Stock Exchange
Deci | May 13, 2022
Deci, the deep learning company harnessing AI to build AI, today launched Version 2.0 of its deep learning development platform, making it easier than ever before for AI developers to build, optimize, and deploy computer vision models on any hardware and environment including cloud, edge and mobile with outstanding accuracy and runtime performance.
AI developers face an uphill struggle developing production-ready deep learning models for deployment. These challenges can be largely attributed to the AI efficiency gap facing the industry in which algorithms are growing more powerful and complex, but available compute power is not keeping pace. This gap also creates financial barriers by making the deep learning development and processing more cumbersome and expensive.
While Neural Architecture Search (NAS) has been presented as a potential solution to automate the design of superior artificial neural networks that can outperform manually-designed architectures, the resource requirements to operate such technology is excessive. To date, NAS has only been successfully implemented by tech giants like Google, Microsoft and in the confines of academia, proving its impracticality for the vast majority of developers.
In order to solve this problem, Deci’s platform, powered by its proprietary NAS engine called AutoNAC (Automated Neural Architecture Construction), enables AI developers to automatically and affordably build efficient computer vision models that deliver the highest accuracy for any given inference hardware, speed, size and targets. Models generated by Deci outperform other known state-of-the-art (SOTA) architectures by a factor of 3x-10x.
Developers can start their projects with pre-trained and optimized models (DeciNets) that were generated by the AutoNAC engine for a wide range of hardware and computer vision tasks or use the AutoNAC engine to generate more custom architectures that are tailored for their specific use-cases. In addition, the platform supports teams with a wide range of tools required to develop deep learning-based applications including a hardware-aware model zoo to easily select and benchmark models and hardware, SuperGradients - an open source training library with proven recipes for faster training, automated runtime optimizations, model packaging and more.
By using Deci’s platform, AI developers achieve improved inference performance and efficiency to enable deployment on resource constrained edge devices, maximize hardware utilization and reduce training and inference cost. The entire development cycle is shortened and the uncertainty of how the model will deploy on the inference hardware is eliminated.
“The new version of Deci’s deep learning platform makes hardware-aware NAS technology accessible to AI teams of any size, helping them eliminate complexities and focus on what they do best - build innovative computer vision applications. We take pride in the fact that the deep learning models generated by Deci's platform are powering AI-based applications of some of the leading enterprises worldwide. We are excited to unleash this powerful engine to help make computer vision even more widely available. Only then can we truly achieve a world where AI advances humanity without limitations, finally making AI affordable, accessible and scalable for all.”
Yonatan Geifman, co-founder and CEO of Deci
With Deci’s Version 2.0 platform, AI developers can:
Easily benchmark models and inference hardware: With Deci’s hardware-aware model zoo, developers can quickly measure inference time of pre-trained and optimized models on and various hardware including edge devices via Deci’s SaaS platform. Simplify the hardware and model selection process by eliminating the need to manually setup and test various combinations of models and hardware.
Generate Tailored SOTA CNN Architectures: Automatically find accurate & efficient architectures tailored for the application, hardware and performance targets with Deci’s AutoNAC engine.
Simplify Training with SuperGradients: Leverage proven hyperparameter recipes and with Deci’s PyTorch based open source training library called SuperGradients.
Automated Runtime Optimization: Automatically compile and quantize your models and evaluate different production settings.
Deploy with a Few Lines of Code: Developers can deploy their deep learning workloads on any environment with the Deci’s python based inference engine.
Deci’s platform includes three tiers:
Free Community Tier: For data scientists and ML engineers looking to find the best models, simplify hardware evaluation and boost runtime performance.
Professional Tier: For deep learning teams looking to quickly achieve production grade inference performance and shorten development time.
Enterprise Tier: For deep learning experts looking to meet specific performance goals for highly customized use cases.
Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company's deep learning development platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment including cloud, edge, and mobile, allowing them to revolutionize industries with innovative products. The platform is powered by Deci's proprietary automated Neural Architecture Construction technology (AutoNAC), which automatically generates and optimizes deep learning models' architecture and allows teams to accelerate inference performance, enable new use cases on limited hardware, shorten development cycles and reduce computing costs. Founded by Yonatan Geifman, Jonathan Elial, and Professor Ran El-Yaniv, Deci's team of deep learning engineers and scientists are dedicated to eliminating production-related bottlenecks across the AI lifecycle.