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$GTCH June 06, 2022 (GLOBE 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.