Synopsys Acquires AI-Powered, Real-Time Performance Optimization Leader Concertio

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Acquisition Augments the SiliconMAX Silicon Lifecycle Management Platform, Enriching In-Field Chip and System Dynamic Optimization

MOUNTAIN VIEW, Calif. -- Nov. 1, 2021 -- To enrich its silicon lifecycle management solution with real-time, in-field optimization technologies, Synopsys, Inc. (NASDAQ: SNPS) today announced that it has acquired Concertio Inc., the leading provider of AI-powered performance optimization software. The acquisition is another step in the continued enhancement of the Synopsys SiliconMAX™ Silicon Lifecycle Management (SLM) Platform. The terms of the deal, which are not material to Synopsys or its financials, are not being disclosed.

"There is little doubt that Silicon Lifecycle Management is becoming critical to the successful deployment and operation of advanced electronic systems," said Shankar Krishnamoorthy, general manager, Silicon Realization Group at Synopsys. "This acquisition of Concertio reflects our commitment to aggressively expand the capabilities and benefits of the SiliconMAX SLM platform to address our customers' rapidly evolving silicon and system health needs."

Synopsys is continually augmenting its SiliconMAX SLM platform to further maximize all aspects of silicon performance across the entire lifecycle, from design implementation through manufacturing, product ramp, production test and in-field operation. The platform is seamlessly integrated with the industry-leading Synopsys TestMAX™ family of test products and technologies, taking advantage of features such as key automation, including early RTL analysis, seamless IP integration and high-speed data access support. Fundamental links to the Synopsys Fusion Design Platform™ provide the unique ability to minimize and rapidly root-cause parametric yield failures due to silicon variation. These links also ensure that SLM solutions have minimal impact on design flows and schedules.

Unique Capabilities Added to SiliconMAX SLM Platform

The growing demand for compute resources at the edge, such as in automotive and industrial IoT devices, as well as in cloud-based data centers, is pushing the boundaries of what today's systems can provide. Boosting the performance and resilience of these existing systems is critical. Concertio achieves this through unique AI-powered dynamic performance optimization software. An autonomous software agent that is installed on the target system continuously monitors the interactions between operating applications and the underlying system environment. The agent's AI capability then gradually learns about the behavior of the applications through reinforcement learning techniques. Using this information, the agent's optimization engine can adapt and reconfigure the system dynamically, resulting in a self-tuning system that is always optimized for its current usage.

Concertio's technology brings to the SiliconMAX platform support for seamless secured integration of AI at the Edge with big data analytics in the cloud. The combination of Concertio's software-related dynamic optimization solution with the hardware-related optimization achieved with sensor-based analytics in the SiliconMAX platform delivers a powerful, unique, and comprehensive system optimization solution. Learn more about the SiliconMAX SLM platform.

About Synopsys

Synopsys, Inc. (Nasdaq: SNPS) is the Silicon to Software™ partner for innovative companies developing the electronic products and software applications we rely on every day. As the world's 15th largest software company, Synopsys has a long history of being a global leader in electronic design automation (EDA) and semiconductor IP and is also growing its leadership in software security and quality solutions. Whether you're a system-on-chip (SoC) designer creating advanced semiconductors, or a software developer writing applications that require the highest security and quality, Synopsys has the solutions needed to deliver innovative, high-quality, secure products. Learn more at www.synopsys.com.

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