On the 29th of May 2022, one of SiMa.ai revealed a new business stratagem. The cutting-edge leading machine leading solution provider, SiMa.ai started developing a system-on-chip platform for AI applications. Meanwhile, the total capital of the company raised to $150 million as the company secured an additional investment of $30 million from Fidelity Management & Research Company, vis-à-vis the involvement of Lip-Bu Tan, who’ll join the board along with other major investors that company has bagged previously.
SiMa.AI wants to democratize machine learning through a simple API, similar to Google’s TensorFlow or Netflix’s streaming infrastructure at scale. They want to make that AI application software open source and provide developers with a cloud to store it. This gives third parties or anyone else who wants access to the cutting-edge research capabilities of Microsoft and Azure the ability to push their own development teams around using high-performance silicon as well. They want to encourage experimentation as soon as possible. But more than just your code is important to this process.
One of the major features of the SiMa.AI platform is its architecture which uses multiple cores and advanced techniques to keep power out of the way so users can experiment on-and-off limits. So instead of designing processors that can run off the shelf, SiMa.AI engineers designed their own algorithms and algorithms that are used in real scenarios over millions of computers around the globe. These algorithms are based on neural networks with state-of-the-art training data sources. With time, these algorithms are refined by trained systems, helping to improve how well computers perform. Eventually, this will make manufacturing less expensive and more efficient.
They have been doing some initial deployments in our corporate environment since August 2021 with huge success rates. Their customers have given them positive feedback and their responses have been encouraging. The goal of the company seems to grow quickly to keep pace with the globally changing ecosphere of AI innovation.
When Intel introduced its first mobile system on a chip about five years ago, it brought an entirely new level of performance to both enterprise and consumer devices. It now powers more than half of all smartphone chips today, but many companies still haven't found the market for their next big leap forward in terms of performance. That has made them turn to silicon to enable the next wave of computing innovations. In January 2021, the company called SiMa.ai came along – a startup developing what they claim will be the world's fastest and most energy-efficient processor system on a chip platform for artificial intelligence applications.
If these results from SiMa.ai’s endeavors are accurate then they should set the stage for a major shift towards a system-on-chip design approach. Once you use a processor on the production line, it's hard to get to the point where it starts paying the dividends in a day or two. One needs to get off to a place like China to see the full picture, get to the other side of that fence, and back home before competitors catch up. There's a lot of room in the space between the public and private sectors to move forward.
According to Krishna Rangasayee, founder and CEO of SiMa.ai, the development of a system-on-chip platform combining artificial intelligence, machine learning, and the Internet of Things (IoTs) will facilitate developers seeking an end-to-end solution that's not only easy to integrate but also facilitates performance needed for the modern applications.
While many companies are already tracking their data on the cloud, they are still looking for ways to bring machine learning to the edge. SiM.ai is committed to helping customers build an ecosystem where their customers will get these benefits — the ability to train models offline, use those models in real-time applications, and get inference throughput of 3-10x compared to using cloud-based training. They believe this would be a way forward for the wide-scale adoption of ML/AI technologies.
SiMa.ai aims to make ML accessible to everyone, from the smallest sensor node to the largest edge system. They're working with companies developing driverless cars, robots, and much more. The company is determined to make machine learning more affordable for embedded and low-power applications by developing a solution that can run on a single GPU at less than 1W of power and no cooling. The company says it has developed a platform that lets developers easily scale machine learning and deploy it on any device.
The company has developed a platform for hardware-enabled AI algorithms that embed into chips and systems, lowering the barriers to entry on machine learning in the embedded space. With SiMa.ai's SoC, customers can now roll out machine learning-driven products in less time using pre-trained models, while requiring only simple coding for complex applications such as autonomous vehicles and devices equipped with deep learning algorithms for medical diagnostics and personalization.
SiMa.ai was found to bring the benefits of machine learning to embedded edge devices. They provide a cloud-based system-on-chip platform that enables end users to achieve state-of-the-art performance, communicate with users in real time, learn from past interactions and improve continuously.
A lot of skepticism can stem from our previous experience with certain technology trends. A prime example is a dramatic rise in CPU prices over the last couple of decades that accelerated the commoditization of everything from the web to desktop computers. As we became more digitally dependent on each other and the market at large, our digital society evolved and in no time digital transformation became the key for companies to grow. With time, we’ll all understand the limitations and proper implications of using the system-on-chip platforms for AI applications. All we can say right now is, that we must thank God for SiMa.AI, for their endeavors.
SiMa.ai is a machine learning company delivering the industry’s first software-centric purpose-built MLSoC™ platform. By allowing customers to address any computer vision problem with push-button performance, we enable effortless ML deployment and scaling at the embedded edge. They enable easy deployment and scaling of ML on embedded platforms by allowing customers to address any computer vision problem while achieving push button performance. The company was founded in 2018 and is headquartered in San Jose, California specializing in machine learning, computer visions, and embedded edge.