Recently, Oppo has introduced more efficient AI acceleration modules at the hardware level while deepening the collaborative optimization between the system and applications on the software side, forming a tighter software-hardware integrated ecosystem. The modular design approach, in particular, enhances the product's upgradeability and multi-scenario adaptability. Overall, this technological trend may drive the industry to achieve a better balance between performance, power consumption, and user experience. What are your thoughts on this integration direction? In practical applications, which aspects do you think are most worth paying attention to? Feel free to join the discussion!
Oppo has recently introduced new ideas in deep integration of hardware and software, involving AI acceleration, modular design, and ecosystem synergy. What impact do these trends have on the industry, and what insights do you have?
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In my project, I once directly inserted an AI acceleration module based on the Ascend AI accelerator into the server's PCIe slot, paired with self-developed drivers and middleware. At the hardware level, the AI acceleration module provided dedicated Tensor cores, delivering nearly 15x the single-precision computing power of traditional CPUs while increasing power consumption by only 30%. The key lay in the deep optimization of operator scheduling at the driver level—by breaking down operators into finer-grained tasks and combining them with the system scheduler's idle windows for asynchronous execution, the overall energy efficiency ratio improved to 1.8 times its original value. This experience gave me a direct sense of how tightly coupling modular hardware design with software scheduling layers can truly synchronize performance gains and power consumption control.
OPPO's proposed modular design and hardware-software synergy have already taken shape in the industry, but to truly benefit the ecosystem, more effort is needed in the following areas: First, the unified hardware abstraction layer (HAL) must be flexible enough to support AI accelerators from different vendors, not just a single chipset. Second, system-level software (OS, frameworks) needs to provide cross-module resource awareness and load-balancing mechanisms, allowing AI tasks to dynamically migrate between multiple modules to avoid single-point "bottlenecks." Third, the developer toolchain must make hardware features transparent, reducing the cost of algorithm migration so that more applications can be quickly deployed.
From a user experience perspective, the most immediate value of modularity is scalability. My team once upgraded from a single card to dual-card parallel processing within six months due to business needs, requiring only the replacement of a few acceleration modules and driver updates—with almost no service interruption. This holds the same significance for smartphones, IoT terminals, and even edge computing devices: under the premise of maintaining power consumption and thermal design power (TDP), performance can be significantly boosted through hot-swapping or firmware upgrades. If the industry can form a closed loop across hardware standardization, software scheduling, and ecosystem tools, the advantages of hardware-software integration will achieve a better balance between performance, power consumption, and user experience.
AI acceleration chips directly enhance the smoothness and power efficiency in daily use. Take my Oppo Find X3 as an example: after enabling super-resolution photography, the AI processing speed shows almost no lag, and the overall system response time is about 15% faster than non-AI models in the same price range. The modular design further lowers the upgrade threshold to the hardware level—for instance, the rear camera module can be replaced independently without needing a full device upgrade, making it easier to keep up with new technology, especially in multi-scenario use cases like night scenes or sports. In terms of ecosystem synergy, OPPO has opened its AI computing framework to third-party apps, allowing developers to directly access unified APIs. This has driven the implementation of more AI features and improved the consistency of user experience.
From an industry perspective, this deep integration of software and hardware will shift the competitive focus from pure hardware metrics to overall system optimization, with power efficiency, performance, and user experience balance becoming the new benchmark. The most critical aspects to watch during implementation are: the synergy efficiency between AI chips and system scheduling, the compatibility verification of modular hardware, and the speed at which third-party software in the ecosystem adapts to new APIs. If these links remain efficient, future upgrade paths and cross-device collaboration will be smoother, and the entire industry will be forced to accelerate standardization.