In recent years, the applications of artificial intelligence (AI) have become prevalent all around the world. And when applied to Edge Computing, the machine learning algorithm is able to process the data generated by a hardware device at the local level without internet connection. Craig Wright, Managing Director of outsourcing advisory firm Pace Harmon states that Edge Computing is “becoming a primary consideration for organizations defining new cloud-based products or services that exploit local processing, storage, and security capabilities at the edge of the network through the billions of smart objects known as edge devices.” With the advantages of costs and speed, this is why the future of the Edge Computing market is so promising.
DeepMentor’s goal as a company is to popularise cloud AI and spread it to every aspect of life while still keeping it cost-effective and energy-efficient. The company provides subscription based SaaS services with miniaturised AI algorithms. They focus on providing the solutions that can truly deploy complex AI algorithms running on cloud or GPU to Edge Devices.
The company found that while Complex AI provides high accuracy, it requires GPU and consumes electricity and generates lots of heat, making it unsuitable for edge devices. Besides, most Edge AI applications can only deploy lightweight and simple AI models with limited capability to deal with changes in the environment, thus resulting in a loss of accuracy.
DeepMentor focuses on AI users, makers, developers and their communities as its target audience. Naturally, there are other companies competing for a share in the market. A notable competitor would be Xnor, which has been acquired by Apple. Although their solutions can miniaturise AI algorithms to 1 bit, it is unable to deal with maintaining accuracy levels, resulting in only 75% of original accuracy retained.
On the other hand, DeepMentor’s technology is able to deploy BigAI on Edge devices with high accuracy. The company’s Edge AI Computing Total Solution can solve the aforementioned issues and tremendously reduce the data calculation times while still sustaining accuracy levels at above 99%.
DeepMentor’s team has more than 10 years of experience in EDA tools and HLS tech. Founder Jack Wu has great expertise in AI development, with over 10 years of theoretical research and nearly 20 years of experience and know-how in IC system design, embedded system implementation and EDA automation system design process. With him at the helm, the company has achieved 6 international patents, 24 articles in major scholarly journals and has over 20 ongoing projects. They’ve also achieved awards in programmes like the 2021 Google Taiwan Hatcher Mentorship Program and 2020 AI Plus Contest Award.
True to their never-say-die approach to work, the team at DeepMentor didn’t let the pandemic get the better of them. They attended online exhibitions and trade shows to share their technology and solutions to explore potential foreign partners and customers. They also took the time to modify their business model from one that was project-based to a SaaS one. Resources were also invested to develop the SaaS platform and miniaturise more AI models.
The company aims to complete their SaaS portal this month and formally launch it in October this year with marketing investments. They also aim to achieve their first quarter subscription target in Q4 2021.
In terms of foreign market expansion, DeepMentor is looking towards the Japanese market. They've engaged with three to five qualified Japanese partners (VAR & SI) from 2021 who are currently expecting DeepMentor’s Saas and Edge platforms. By building and maintaining good ties with Japanese clients, the company hopes to continue to extend such relationships and build use cases in smart manufacturing and autonomous robotic processes.