Lenovo Selects Synaptics Katana Ultra-Low-Power AI Solution for Yoga Tab 11

Tablet delivers premium voice communication, and voice assistant features leveraging advanced neural network technology

CES® 2022 – Synaptics Incorporated  announced that its Katana platform drives AI-enhanced voice processing in the Lenovo Yoga Tab 11, the latest member of the Yoga Family of tablets. The Katana system-on-chip (SoC) uses proprietary neural network-enabled hardware and algorithms to clearly discern and capture user voices and related commands in challenging environments to ensure a robust voice assistant experience in Lenovo’s Yoga 11.

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With its AI-enabled far-field noise suppression technology, the Katana SoC is specifically trained to remove extraneous sounds associated with challenging home and office environments. Audio clarity is ensured through a proprietary four-speaker acoustic echo cancellation (AEC) algorithm. The solution supports all major worldwide automated speech recognition (ASR) engines to clearly hear voice commands even amid ambient sounds and loud audio playback on the Yoga itself. Further, the Katana-based solution delivers premium voice quality while consuming extremely low power, extending both battery life and use time between recharges.

“Lenovo adopted the Katana low-power AI solution from Synaptics for our Yoga Family of tablets, continuing a long and successful relationship between the two companies,” said Tony Chen, Tablet Business VP at Lenovo. “We look forward to continued collaboration with Synaptics, extending our partnership across touchpad, fingerprint, video interface, audio, and now ultra-low-power AI technology powering premium Lenovo product lines.”

“The Katana platform was designed to deliver energy-efficient premium products with excellent performance at low system cost while being highly versatile and scalable,” said Venkat Kodavati, SVP & Chief Product Officer at Synaptics. “Lenovo leveraged Katana’s strengths in the Yoga Tab 11 to provide a single platform that meets current requirements while accommodating future low-power AI features, such as biometric identification and localized speech recognition.

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