SAN DIEGO, CA - June 6, 2022 (Investorideas.com Newswire) GBT Technologies Inc. (OTC PINK: GTCH) ("GBT" or the "Company"), is researching the development of a machine learning-driven, automated integrated circuits design environment, enabling Fast-Track, Design-to-Silicon capabilities. The research is focused on an unified environment that contemplates including architecture design, functional verification, full-system power analysis and comprehensive physical verification. The goal of the design environment is to improve design performance by analyzing latency area optimization, bandwidth and power. The solution that the Company is researching is aimed to support custom analog, mixed, radio frequency and synthesis designs. In addition, the Company intends that the new system will perform signoff extraction, static timing analysis (STA), robust physical verification including design for manufacturing (DFM) and electromigration.
Typically, the industry is using separate electronic design automation (EDA) tools for specific topic which creates vast integration efforts. GBT plans to offer a one-shop stop for the entire IC design flow. GBT believes that a comprehensive, highly automated flow, would enable fast-track for an IC design project by combining traditionally separate front-end and back-end chip design technologies, into one integrated flow. The automated flow is being designed to eliminate iterations between EDA tools, accelerating the design cycle and reducing the overall IC development time and costs. The system plans to support mixed signals System on a Chip (SoC), digital cores and analog IPs. The research is examining the use of the GBT's machine learning-driven accelerators to dramatically enhance design productivity and enabling design reuse in the design environment. Deep learning algorithms will aim to provide rapid design capabilities by analyzing and optimizing circuit designs and layouts. The system will take into consideration the process design rules, reliability constraints, DFM and thermal analysis aspects. GBT believes that its research will illustrate that this type of a machine learning-driven, unified, IC design environment will provide a quantum leap in efficiency and productivity for microchip's designers, significantly reducing the overall IC's design time.
"We are researching an IC solution that would target small start-up companies to large corporations in the semiconductor industry with varying design requirements. With GBT's new approach, we aim to develop a machine learning-driven, one automated IC design flow, enabling Fast-Track Design-to-Silicon for IC design houses. The way we want to do this is by combining traditionally separate front-end and back-end chip design flows into one integrated environment that accelerates the overall design cycle and reducing the IC development costs. A typical microchip design process includes many steps which are classified as front-end and back-end tasks. Various steps are executed using separate EDA tools which require vast amount of integration and design environment adjustments. The new, machine learning-driven flow that we are researching aims to provide one-stop design environment advanced capabilities, with high levels of automation, with the goal of enabling the delivery of superior quality designs, with much faster completion time. The usage of our deep learning and advanced computational geometry algorithms aims to produce a comprehensive design environment, enabling efficient digital/analog design and implementation, particularly with advanced manufacturing nodes. In addition, we are also researching incorporating other capabilities into the system which may include functional and physical verification, simulations, power optimization, characterization, or yield management. With this research we aim to standardize digital and analog IC's design, simulation, verification and characterization. We firmly believe that a one, intelligent, automated IC design environment will introduce a significant productivity enhancement for IC design firms, reducing their overall projects design time and bringing them faster to market," stated Danny Rittman, the Company's CTO.
There is no guarantee that the Company will be successful in researching, developing or implementing this system. In order to successfully implement this concept, the Company will need to raise adequate capital to support its research and, if successfully researched, developed and granted regulatory approval, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.
GBT Technologies, Inc. (OTC PINK: GTCH) ("GBT") (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBT's mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBT's goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBT's vision is to produce this system as a low cost, secure, private-mesh-network between any and all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.
Certain statements contained in this press release may constitute "forward-looking statements". Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements as a result of various important factors as disclosed in our filings with the Securities and Exchange Commission located at their website (http://www.sec.gov). In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic conditions, governmental and public policy changes, the Company's ability to raise capital on acceptable terms, if at all, the Company's successful development of its products and the integration into its existing products and the commercial acceptance of the Company's products. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of the press release.
Dr. Danny Rittman, CTO
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