Recent Advancements and Challenges in Embedded Deep Learning
The innovation of deep learning technology has created for us a world of unending possibilities. Perhaps the most amazing factor, like other technology related creations in our world, is the fact that there is an unending world of possibilities and potential. Researchers are constantly seeking to exploit newer applications of deep learning technology, and this helps to create an exciting world where anything is possible. The ability for computers to assimilate like humans is something that people could have only imagined decades ago. However, this is becoming more and more possible thanks to the development of deep learning technology.
What Is Embedded Deep Learning?
Embedded technology implies the use of computer systems or devices that serve a specific function within a larger device. Most of these systems created with such frameworks have constraints when it comes real-time computing, but there are still a lot of benefits to be realized. There are a lot of devices today that make use of embedded technology (e.g. SoC). The prospect for embedded deep learning revolves around devices that are specifically designed for deep learning technology. A lot of tech companies are making use of these systems today. Baidu for example (which is one of the companies at the forefront of GPU based approaches to deep learning), built a GPU based calculator which was specifically designed for deep learning purposes.
Deep learning has become a module for solving computer vision problems, but the sheer intensiveness of its application and requirements for large memory bases make it very challenging to achieve on embedded platforms that work on low power. This serves to be a minor setback in the potential for deep learning to transform our world. Thankfully, there are new advancements in the world of embedded deep learning everyday that push us one step further.
There are a number of challenges that the development of this technology is confronted by on a daily basis, but constant innovation and research will see us eclipsing these restraints in the near future. We will therefore be taking a look at some of the recent advancements in the world of embedded deep learning, while highlighting the challenges that this technology is currently facing, seeking out ways to overcome them.
Recent Advancements and Challenges in the world of Embedded Deep Learning
There have been a lot of advancements in recent times in embedded deep learning technology. Deep learning requires a lot of computing and storage since it is data driven. This means that storage problems are always going to be an issue when it comes to embedded deep learning. The latest CPUs that we use on a daily basis still struggle to perform optimally when they are faced with real time algorithms from these sort of applications that rely on vast data sources.
This means that a welcome advancement in the world of deep learning is definitely one of improved data storage capabilities.
A welcome improvement in this technology is that of GPUs, which are parallel computing machines that are able to handle large amounts of data. There was also significant advancement in this form of technology when researchers began to prove that the neural networks in deep learning can perform even better when it comes to computer vision tasks, as opposed to the traditional methods of machine learning algos. This gradually led to a shift in the focus of the industry. Increased funding in deep learning research has also led to the possibility of discovering even more about this technology and developing upon it.
A lot of computer automation tasks will particularly be in the front seat when benefiting from deep learning advancements. In the near future, there will be solid growth in industrial automation, educational implications and even biomedical. This means that we would be experiencing a larger amount of data to be dealt with on a daily basis, and a faster infrastructure base for computing. With all of these advancements, it is highly likely that algos from deep learning will begin to outdo the basic automation tasks, and will lead to a total reorganization of industries.
With all of these advancements in embedded deep learning technology, it is only natural to envisage a world of possibilities to ensue. Data scientists and researchers constantly seek out ways to improve on this technology, but the challenges faced prove to be stumbling blocks. Problems like insufficient data storage and processing will continue to affect deep learning until there are newer ways to incorporate these algorithms.
What does the future hold?
GPU’s are championing the future and rapid development of Artificial Intelligence and deep learning as a whole. Deep learning stands a chance of becoming the future of every form of computation. We are likely to see machines that are becoming more and more intelligent. Machines will have the ability to process information at their disposal, and the constraint that used to be the problem of excessive data with inadequate storage may just be a thing of the past. However, all of this would not be possible if there is not sufficient research in embedded deep learning technology. We just may see a world where smart sensors have begun to be embedded in every surface around us, making it a more interactive world for humans and machines where valuable information is assimilated and recorded by the second. We may also see a world where image and vocal sensors have the ability to analyse the activities going on around us. While it seems like something considered like ‘Big Brother’, it doesn’t have to be.
With all of the challenges in embedded deep learning technology, there has definitely been a number of relevant advancements. These advancements would go on to shape our future and the world around us.