Google has developed a custom-built System on a Chip (SoC), Tensor, to power Pixel phones. “So excited to share our new custom Google Tensor chip, which has been 4 yrs in the making ( for scale)! Tensor builds off of our 2 decades of computing experience and it’s our biggest innovation in Pixel to date. Will be on Pixel 6 + Pixel 6 Pro in fall,” CEO Sundar Pichai tweeted.
After Apple ( M1), Huawei (Kirin), Samsung (Exynos), now Google has joined the in-house SoC club. The advantages of Tensor include:
- More computing power: Google’s Pixel uses computational photography and ML to capture images (Night Sight, for example). The tech giant also introduced powerful speech recognition models for its devices. The features require high computational power and low latency for the best performance. Tensor can bring in complex AI innovations to Pixel smartphones.
- Unlocking new AI features: Tensor chips give Google the freedom to bring in new ML-based features without worrying about the performance. Robust processors are a prerequisite to run heavy AI workloads.
- More layers to hardware security: Tensor new security core along with Titan M2 will work as an added layer of security. Titan M from Google is a custom-built chip to protect sensitive data such as passcode, enable encryption, and secure transactions in apps.
The recent release of the first beta version of Android 12 provided Pixel users with personalised features such as notification shade, volume control and lock screen. New features in Android 12 provide more transparency into ‘which apps are accessing what data’ and more control to the users to make informed decisions about how much private information apps can access.
The processor is crucial to the phone’s performance and battery life. Despite owning Android OS, Google was unable to put a dent on the smartphone market. With the all-new Tensor chips, the Mountain View giant is looking to revitalise its smartphone segment. Of late, Google has made a litany of innovations in the field of artificial intelligence and machine learning.
Image Credits: Google
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