Graphic processing units (GPUs) are being increasingly termed as the new computer processing units (CPUs) in the era of cutting-edge technologies along the likes of AI, big data and machine learning (ML). Indeed, a general consensus among industry experts is that these 3D graphics cards have turned around the fortunes of a fairly dull personal computer market as the world witnessed widespread adoption of the 32-bit operating systems in the mid-90s.
The global GPU market,slated to accrue $80bn by 2024 by Global Market Insights, has come a long way since its inception, owing to the fast-paced evolution of modern graphics processor since the introduction of the first 3D add-in graphics card in 1995. In the current times, GPU-powered chips are revolutionizing a plethora of business verticals and are adding a unique dimension to the way technology is being developed and applied.
The increasing role of GPU-powered AI platforms in honing autonomous vehicle technology
Owing to its cost-efficiency and immense popularity, GPU has emerged as the most dominant chip architecture for self-driving technology in the recent past. The increasing complexities of computing hardware and the requirements for testing autonomous cars on real roads warrant superior AI-based operating platforms that would anticipate potential hazards while driving. In this regard, world's foremost GPU maker Nvidia has been scoring big wins in terms of developing GPU-powered AI platforms and teaming up with well-known automotive giants.
After introducing its original AI-based supercomputer platform Drive PX in 2015, the US-based Nvidia has recently come up with a new iteration of the platform named Pegasus which can be utilized to power Level 5 autonomy. This new platform is able to support fully autonomous cars without pedals, steering wheels or mirrors. It has been built on Nvidia's CUDA GPUs which has intensified its computing speed by 10 times and lowered the power consumption by 16 times.
Equipped with these Level 5-empowering GPUs, the driverless cars would most likely be deployed in a ride-hailing capacity in restricted settings like airports or college campuses. Moreover, it has also been reported that German engineering and electronics company Robert Bosch GmbH and leading automaker Daimler AG have partnered up with Nvidia to utilize its Pegasus system as the platform for their self-driving vehicle designs beginning in 2020. A few other automotive firms such as Zenrin, ZF and Audi have committed to use the AI-based computers of Nvidia.
Considering the instances of GPU makers building new products, particularly Nvidia, it can certainly be claimed that the criticality of GPU-powered AI platforms in the effective implementation of autonomous vehicles programs is of much significance. Many more such developments are in the works and would speed up the creation of AI-driven big data systems, in which GPUs would play a pivotal role in the upcoming years.
Transforming the IT infrastructure of healthcare sector with high-end graphic processors
Bearing in mind the way recent economic trends have been unfolding, it is quite difficult to quantify and measure the impact of new-age technologies such as IoT, AI, machine learning and deep learning on the product offerings and growth of almost all business verticals. However, it is certain that the ability of these technologies to identify common patterns for decision making and make sense of big data can be rightly termed as awe-inspiring. Out of these technologies, deep learning is one of the most significant branches of AI which is taking the world by storm with its ability to successfully interpret and provide crucial insights from massive amounts of data.
In this context, it is quite imperative to take note of the fact that healthcare institutions across the globe are churning out data on a scale that it simply is too humongous for manual processing to be a feasible method. In combination with graphics processors, which have now evolved to offer extremely high processing power, deep learning is transforming medical research, treatment, and patient services by turning the 'black box' of big data into effective solutions.
While taking stock of the current scenario pertaining to the AI in healthcare, it is particularly essential to mention that tech giants such as Google, Apple and Tencent are creating GPU-accelerated products to improve the predictive analytics domain of global healthcare regime. These firms have already embarked upon major projects that would utilize advanced graphic processors in their AI software to strengthen neural networks and speed up the pace of these machines in learning and improving automatically. Moreover, these initiatives would assist healthcare organizations in streamlining the huge amounts of data being generated in the present times.
Elaborating on a few of such initiatives, Google has recently acquired DeepMind's healthcare division to build "an AI assistant for physicians and nurses". This is in addition to the tech behemoth's quest to improve accessibility of electronic medical records (EMR) which is likely to become a crucial element in disrupting the healthcare landscape using AI.
Amazon is another prominent firm that is hoping to decode the information in haphazard writing like medical records or even physician's notes. In 2018, the e-commerce giant unveiled a new ML service Amazon Comprehend Medical that would use natural language processing to interpret the medical records.
Given the sheer ML and engineering prowess of Google and the willingness of other tech leaders like Amazon to build new products with help of GPU-powered AI platforms, the healthcare sector is on the cusp of witnessing a change from early diagnosis to medical records, to predicting the outcomes of numerous complex treatments beforehand.
For most computer users, GPUs are remnant of the video cards that were designed for high-end, graphic intensive games. These were solely optional, which did not impact the buying decision of an average user investing in a server or personal computer. However, these specialized processors have now carved out a new place for themselves in the computing world and are powering the technology which supports the development of autonomous cars, speech recognition, cancer diagnosis and numerous other intelligent use cases.