Robert Triggs / Android Authority
TL;DR
- Oppo is reportedly developing custom chipsets for premium phones.
- The first phones with these processors are slated to arrive by 2023 at the earliest.
Custom processors are one of the biggest challenges for smartphone brands, with only Apple, Google, Samsung, and Huawei having flagship-level in-house silicon at this juncture. However, we’ve heard murmurings in the last year or two about Oppo working on custom chipsets of its own.
Now, Nikkei Asia has reported that the company is indeed developing high-end chipsets for its “premium” smartphones. Oppo plans to use these chipsets in phones launching in 2023 or 2024, two sources briefed on the topic told the outlet.
Oppo is apparently considering the use of TSMC’s 3nm design for these new chipsets. Further technical details weren’t forthcoming, but it’s a safe bet that Arm’s high-end CPU cores and GPUs will be used. The company will likely have to design parts like imaging chips and modems too, although we’ve seen Google seemingly adopt Samsung’s modem for its own in-house Tensor processor.
Why would Oppo take this route?
The news comes over a year after an apparent internal Oppo memo leaked which pointed to a so-called Mariana Plan. The memo purportedly confirmed the existence of a custom processor initiative within the company and the intention to spend $7 billion over three years on these efforts. It was also claimed that OnePlus and Realme engineers were working on the project, suggesting that phones from these brands could offer custom silicon down the line too.
Either way, we can see why Oppo and other smartphone brands might want to get into this arena. Custom chipsets allow OEMs to offer greater control over their supply chain, especially in light of the global chip shortage and the US trade ban against Huawei.
Custom silicon also enables mobile manufacturers to better tailor the hardware for the experience and features they have in mind. We’ve already seen this with Google and the Pixel 6, as the company adopts dedicated machine learning silicon for offline inference and improved imaging features.
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