Tenstorrent achieves best-in-class PPA and First-Pass Silicon Success for Datacenter AI Processor SoC with INVECAS advanced ASIC engineering capabilities

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invecas.com, Oct. 21, 2020 – 

Santa Clara, USA & Hyderabad, INDIA Oct 21, 2020–INVECAS Inc, a leading provider of ASIC design solutions and Tenstorrent Inc, a leading AI/ML processor company, announced today that Tenstorrent has achieved first-pass silicon success for its recently announced Grayskull AI processor system-on-chip (SOC) utilizing INVECAS Advanced Chip Builder Tool (ACT™) physical implementation flow to achieve predictable design convergence, best in class PPA and accelerate time-to-tape-out for complex design.

INVECAS provides ASIC design solutions across all the technology nodes up to 6nm FINFET for key global customers targeting next generation AI, 5G, IoT, automobile and networking markets. The ASICs targeting these market applications are complex designs with multiple hierarchies featuring 1B-2B+ gate count, large number of high-speed interfaces, high performance and low power. INVECAS customers expect faster time to market for their ASIC product deployment, which drives the need for faster design convergence and a reduction in overall design cycle time from netlist to Silicon on these large designs.

INVECAS's experienced Package and Test Design and Development team utilizes the state-of-the-art Simulation and Design tools to achieve clean signal integrity and power integrity for complex high-power products in this space. The ATE Development team is adept at fast implementation of test vectors and test methodologies to ensure reliable first pass silicon; and with key Product Engineering experience and tools we achieve industry leading metrics for high Quality and low DPPM during production.

Tenstorrent, a next-generation computing company bringing to market the first conditional execution architecture for artificial intelligence, recently announced its flagship product: Grayskull. Grayskull delivers significant baseline performance improvements on today's most widely used machine learning models, and opens up orders-of-magnitude performance gains for current and future ML models by enabling inference and training computations to be tailored at run-time for each input presented to the model.

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