The OPPO Reno 10x Zoom Review: Bezeless Zoom

AIMark makes use of various vendor SDKs to implement the benchmarks. This means that the end-results really aren’t a proper apples-to-apples comparison, however it represents an approach that actually will be used by some vendors in their in-house applications or even some rare third-party app.

In AIMark, the Oppo Reno 10x ends up quite adequately amongst the Snapdragon 855 devices who come shipping with the corresponding Qualcomm libraries to make the benchmark work. We do note that other S855 phones have a slight edge here, likely due to their more optimised software stack.

In the CPU accelerated workloads, we see that the Oppo Reno 10x absolutely stands out compared to all other Snapdragon devices, having nothing in common with the rest of the pack in terms of the resulting performance figures. The phone ends up with one performance regression, one test that didn’t complete, but otherwise the performance looks to actually be better than that of what’s available on other Snapdragon 855 phones.

The only explanation that I can offer here is that it looks like rather than using Qualcomm’s NN driver stack, the phone is falling back to the default Android Tensorflow libraries. The interesting thing here is indeed that the phone looks to be performing better with these libraries than that the ones that are shipping with the Qualcomm BSP, something the company should certainly look into.

Unfortunately for the rest of the AI Benchmark tests which make use of NNAPI acceleration for INT8, FP16 and FP32 models, the Reno 10x just didn’t have the correct drivers and thus wasn’t able to perform the tests.

If there’s one thing that’s worse than bad performance, it’s not able to perform a task at all. I’m not sure if the Reno 10x performs any differently in a global firmware version of if this issue is solely a characteristic of the Chinese variant. In any case, I find it quite disappointing for the machine learning ecosystem as Oppo is a major vendor, and the Reno is its flagship device. How are application developers supposed to embrace machine learning when we the software situation still has such enormous draw-backs?