What are the advantages of our neural network inference framework?

1. Background

Let me introduce /ˌɪntrəˈdjuːs/ the background /ˈbækɡraʊnd/ first. Today we will talk about this topic: What are the advantages of our neural network inference framework?

2. What are the advantages of our neural network inference framework?

Dave: Hello, Forever. I heard that the latest Galaxy /ˈɡæləksi/ smartphones are equipped /ɪˈkwɪpt/ with your framework.
Forever: Yeah, sure. In fact, our framework has supported both high-end and low-end Samsung phones since 2014.
Dave: This is a long life cycle for software. Could you introduce your framework to us?
Forever: Yeah. Our team is responsible /rɪˈspɒnsəbl/ for the neural network inference framework on the mobile GPU. As you know, there are many neural network models such as classification models, detection models, stable diffusion models, large language models, and so on. Our framework can parse /pɑːz/ the model, optimize the graph, and then execute the model on the mobile GPU.
Dave: It’s interesting. What are the advantages of your hardware?
Forever: Let me think about it. As we know, neural network models can be executed on CPU, GPU or NPU. For computing tasks, GPUs are more powerful than CPUs. NPUs are specialized /ˈspeʃəlaɪzd/ processors that only support some model. In contrast, GPUs are general-purpose processor that support all models.
Dave: Oh, I see. What are the advantages of your inference framework?
Forever: I almost forgot. Let me describe the advantages of our inference framework.
On one hand, it is a general and reliable /rɪˈlaɪəbl/ inference framework. We are constantly /ˈkɒnstəntli/ improving our code and adding new features.
On the other hand, it is a high-performance inference framework. We keep working on both software and hardware related optimizations so that these models can run on GPU in real-time.
Dave: That’s a awesome /ˈɔːsəm/. I think your framework plays an important role /rəʊl/ for Galaxy smartphones and I hope your software can achieve higher performance.
Forever: Thanks. We have confidence /ˈkɒnfɪdəns/ in achieving this.

GPU stands for graphics processing unit.

References

[1] Yongqiang Cheng, https://yongqiang.blog.csdn.net/