Heterogeneous computing has been gaining popularity recently, owing to its inherent efficiencies and the business impact that it generates. Essentially, what heterogeneous computing does is that it enables one system to have multiple unique computing sub-systems.
One scheme can have different capabilities of executing core instructions, thereby computing in parallel formats. Each unit has its own pre-defined work which blends into the final result. This gives the system core advantages in specific computing capabilities in technical areas such as AI and Big Data. The time and resources savings are immense when companies use heterogeneous computing.
The impact on deep learning and neural networks
When it comes to analyzing information, computing different types of inputs is key. This could be visual information (videos, images, etc.), text (books, web pages) or raw code from a repository. Therefore, processing such volumes of information in an efficient manner requires the utilization of a heterogeneous computing system.
Neural networks are made much more efficient when we introduce heterogeneous computing into architecture. The system can create more meaningful insights from the information provided and run operations at a much faster pace. Raw information can be collected from wearables, servers, websites and customer feedback simultaneously and be fed into the neural map. This helps the AI determine critical characteristics and produce the desired result.
Heterogeneous computing is especially impactful in the arena of deep learning. To extract meaningful patterns from raw data requires higher computing capabilities than traditional models. Through iterative optimization and data computing, supercomputers are being leveraged to expedite the process.
In fact, Heterogeneous computing is being supported by some of the leading manufacturers including AMD, Nvidia, and Qualcomm. From facial recognition to medical diagnosis, heterogeneous computing is enabling efficient AI processing.
Real-time extraction of insights
NASA’s Frontier Development Lab is using IBM Power9’s heterogeneous computing technology to predict how solar radiation interacts with satellites, GPS systems, and power grids. The constant influx of information is being monitored, while insights are being extracted in real time. This is enabling institutions like NASA to take maximum advantage of AI’s capabilities.
Developers are also looking at how heterogeneous computing can make analytics more accessible to all decision makers. Making the dashboard more simplified requires information to be parsed in a streamlined manner. With the help of heterogeneous computing, companies are able to scale core AI models to extract better insights.
With the increased adoption of heterogeneous computing, core power utilization is rising. This means that there are no physical limitations to what you want to compute. The architecture is designed to integrate multiple machines and systems, thereby giving you the most accurate insights when necessary.
Chip-developers and software designers are working on enhancing the output that companies can receive as well. With the rising demand for more accurate insights, heterogeneous computing is offering a viable model. Industries such as IoT and smart-technology solutions are especially focused on HC as a scalable solution to data analytics.
A truly global approach to AI and Big Data
Companies are designing computing systems to take in data from multiple regions and units. This is enabling them to become truly global when looking at AI and Big Data. There are limited restrictions on how companies are viewing information and can gain access to rich insights at any time.
This has created a global model of information processing as well. Data can be collected from multiple end-points across the world and computing can be done locally or at the central node as well. With the proliferation of the cloud and machine learning, companies are no longer limited in their approach towards AI.
As companies grow, so too does their need for computation and analytics. There are complex systems at play, and it’s important to gain maximum benefits from AI solutions. That’s where heterogeneous computing allows you to become truly ubiquitous in your approach.
Cognitive computing can also learn from insights from IoT data sets, and models that are designed to function within AI substructures. Complex tasks can be performed across a wide variety of functions. This allows companies to think globally when it comes to high-level forms of computing. The scope and scale of AI are enhanced to capture greater capabilities across the domain.
Speed, efficiency and scale
The idea behind greater adoption of AI technologies is to introduce speed and efficiency at scale. This is done to create a more robust model of AI processing which can capture more information at rapid speeds. This can be effectively done using heterogeneous computing as well as designing models, that encapsulate unique processing centers. Supercomputers can be run across various domains to give you quicker results to extend the reach of AI.
Additionally, when it comes to large-scale data collection and management, companies need faster processing systems. This has given rise to the demand for heterogeneous computing, especially in the domain of AI. When there are resource-intensive tasks to be performed simultaneously, heterogeneous computing is the best approach.
Qualcomm has been working on its first AI & 5G robotic platform that takes advantage of high-performance heterogeneous computing. The platform is making it easier for companies to create efficient robots that can help in automating via AI systems. This platform can also be scaled up owing to its computing architecture.
Intel is also developing ‘exascale’ supercomputers that focus on leveraging heterogeneous computing. Its impact on AI is estimated to be significantly greater than past iterations. The speed and efficiency provided are unmatched to alternatives.
In fact, analysts at Gartner believe that heterogeneous computing driven multi-clouds are the way forward for much of technology innovation in the future. It’s enabling greater flexibility across domains and allowing quicker scale development of core AI systems. Additionally, it’s creating a more robust model for all companies to leverage when it comes to AI adoption across the board.
In terms of AI and how heterogeneous computing impacts it, there are many areas to focus on within each domain. The power and flexibility of the architecture can drive incremental results that are miles ahead of expectations.
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