In teasing the Pixel 6, Google very much framed the preview around its first custom-built System on a Chip (SoC). The Google Tensor chip…
In teasing the Pixel 6 at the start of August, Google very much framed the preview around its first custom-built System on a Chip (SoC). At the Pixel Fall Event today, Google fully detailed Tensor and called it the “biggest mobile hardware innovation in the history of the company.” Google’s stated goal for building Tensor is to push what’s possible on smartphones. The company wants to bring “AI breakthroughs directly to Pixel” and drive its vision of always-available technology, i.e. Ambient Computing The former is born out of Google’s hardware division believing that AI-backed smart features are how it can differentiate Pixel against competitors, while Google considers phones the “central control device of an ambient system.” The Pixel Launch Event notably saw Google talk about Ambient Computing again. The last time that occurred in a significant manner was the 2019 Pixel 4 launch. In an interview with The Verge, Rick Osterloh said that work started in 2017 after coming to the realization that Google couldn’t take a piecemeal approach — like building a single co-processor, e.g. Pixel Visual/Neural Core — to boost AI models. Rather, an entire chip that’s optimized for desired tasks is needed. The Tensor chip is specifically designed to offer Google’s latest advances in AI directly on a mobile device. This is an area where we’ve been held back for years, but now, we’re able to open a new chapter in AI-driven smartphone innovation. At the Pixel Launch Event, Google went into Tensor and explicitly touted the inclusion of two high-performance ARM Cortex-X1 cores at 2.8 GHz. They are joined by two “mid” 2.25 GHz A76 CPU cores, with Ars Technica’s Google Silicon interview pointing out how they are based on a 5nm process rather than the 7nm original found in flagship phone chips last year. Four high-efficiency/small A55 cores round out the CPU. The dual-X1 approach allows Google to throw more power at workloads that are of medium intensity. In a normal CPU, the mid cores would handle such tasks, like Google Lens visual analysis, but be “maxed out.
