Hello. I believe the number of companies working with deep learning is increasing nowadays. Let me introduce our latest service.
There are many companies that want to utilize deep learning in their business. The challenges they face include a shortage of machine learning talent and keeping up with the latest trends. Furthermore, commercializing deep learning can be costly and time-consuming, and the prospects can be unclear. Therefore, our company has launched a service to support commercialization through model improvement. Specifically, we modify existing deep learning models to make them more suitable for business by making them lighter and faster.
We employ mathematical methods to achieve this lightweight and fast processing. Our company has been using numerous GPUs and CPUs to perform such speed optimizations through mathematical calculations. This method can be applied to any neural network and can be converted with software alone, so there's no need for hardware changes. By actually performing lightweight and fast processing, we can reduce costs and save resources, greatly contributing to commercialization.
- We achieve lightweight and fast processing by modifying the software.
- No need for hardware changes.
- Reduced costs, enabling implementation in resource-limited environments.
Regarding this lightweight and fast processing, since it is performed using specific models, it does not necessarily guarantee lightweight and fast processing for every problem or model. However, many customers have indeed tried it as part of their business development. Moreover, by combining multiple lightweight and rapid processes, it can also be flexibly applied to custom research and development.
Specifically, we employ two types of lightweight and fast processing methods. One is pruning, which reduces the number of neural network connections to make it lighter and faster. The other is quantization, which speeds up processing by reducing computational precision. These two methods can be used simultaneously.
There are some disadvantages, though. When making the models lighter and faster, it can be difficult to maintain the original accuracy of the heavy models. Of course, depending on the situation, it is possible to lighten and speed up without sacrificing accuracy, but often accuracy is sacrificed. Thus, the balance between the desired accuracy and the cost of resources is a consideration point for business.
For those who want to try out various models, we have many models available for computation, so please feel free to test them. We also provide computational resources such as GPUs, so you can request our services even if you don't own GPUs. We primarily develop with a tool called PyTorch, but we also support other frameworks like TensorFlow.
Our new service, especially released for the computer vision field, is for neural network models commonly used in this area, such as CNNs and Vision Transformers (ViT). The image recognition field has become a large market, and many companies use it for business development. By utilizing various models such as convolutional neural networks and the latest transformer models with Vision Transformers, you can achieve higher performance and more expansive business.
We are releasing the following two services compatible with CNN and ViT:
- blueqat tensor CNN
- blueqat tensor ViT
These services involve neural networks with different internal structures, so you can modify your existing models or use tools provided by us, like the Vision Transformer. Please feel free to contact us as we can flexibly respond to your needs.
We have prepared models that can use pruning and can adjust and customize the base to fit the customer's needs. We can also prepare cloud services, so if you would like to use them in combination, please contact us.
Contact Information:
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That's all. Thank you.