The Automation Acceleration: How GPUs Are Fueling the Future of Work?

Artificial intelligence, machine learning/ deep learning, computer vision, artificial neural networks, and augmented reality/ virtual reality have revolutionized industries. These exceedingly disruptive technologies, in conjunction with real-time data analytics and process automation, have culminated in a momentous shift in man-machine interaction and precipitated “Industry 4.0” aka the Fourth Industrial Revolution. 

And in this transformation to a new industrial age, the Graphical Processing Unit (GPU) is playing a substantial supporting role. The GPU has been an essential component of computing systems for ages and is no longer restricted merely to graphics rendering. It has, in fact, taken up the mantle of a powerful hardware accelerator, breathing life into various AI/ ML applications. A vast majority of these GPU-supported applications perform cumbersome repetitive tasks (such as automatic license plate identification) or undertake lightning-fast assignments that humans can barely comprehend (such as financial data monitoring).

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And now, GPU has effortlessly penetrated another niche segment – process automation. Learn how here. 

The Fourth Industrial Revolution and Process Automation

The Fourth Industrial Revolution, aka Industry 4.0 aka 4IR, represents a paradigm change in how enterprises operate. Industry 4.0 leverages the dramatic changes in technology, societal patterns and methodologies in the 21st century to enhance productivity without excessive human intervention. The boundaries between the digital, physical and biological worlds have blurred, and AI/ ML/ DL, IoT and Blockchain technologies are beginning to be associated with all aspects of the economy, be it finance or cybersecurity or healthcare or even fashion! 

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Most importantly, with increasing digitization, process automation has moved beyond niche segments like robotics, cybersecurity log management and financial spreadsheet management. Not surprisingly, core business principles determining how enterprises operate are expeditiously gravitating towards automation, creating a “Smart Industry”. The Smart Industry is focused on productivity maximization, primarily through better utilization of resources and delegation of repetitive and/or less consequential operations to automated systems trained on extensive AI/ ML models. 

Thus, process automation comprises three different functionalities – 

1. Digitize and automate business operations 

2. Centralize information repositories 

3. Reduce human involvement 

A variety of monotonous and/or time-consuming tasks can be automated across different industries. This is especially applicable to low-effort industrial tasks such as CCTV surveillance, automated license plate identification, monitoring for manufacturing defects and machinery faults, biometric identification, pattern recognition and anomaly detection, etc. 

Most process automation systems now rely on AI/ ML algorithms for targeted training on relevant datasets. This training as well as the eventual deployment of AI-based automation systems leverage GPUs and parallel computation to optimize the performance and overcome errors, false positives and other challenges. More on that later though! 

Benefits of Automation –

Besides enhanced reliability, transparency and accuracy, automation has various other benefits such as – 

1. Productivity improvement –

Cut-throat competition defines the business world we inhabit today. Reduced production on account of traditional manufacturing practices or manual labor inhibits competitivity. Automation ameliorates manual errors, brings about production consistency, and increases the overall level of production manifolds vis-a-vis human efforts. This is not just witnessed in tangible sectors like manufacturing, but also in fields like financial data monitoring and cybersecurity where analyzing event logs manually would consume unbearable manpower. 

2. Accuracy optimization –

Increase in accuracy benefits businesses in the long run. Eliminating manual involvement eliminates human errors. Training ML models on colossal datasets to finetune accuracy, and thereafter deploying them with highly efficient GPU machines can deliver enormous dividends. 

This has applications across industries, e.g., AI-based MSSP solutions can be better trained for triggering cybersecurity breach and compliance adherence alerts, biometrics systems can be developed for targeted delivery of subsidies and government benefits, biomedical and food production facilities can be impeccably maintained against temperature-ventilation fluctuations and contaminations, orthorectification of satellite data can be manifolds sped up, etc. 

Of course, OpenAI’s ChatGPT takes the crown in any discussion about GPU-powered AI/ ML training accuracy – the underlying Large Language Model (LLM) was trained using a whooping 20,000 Nvidia A100 GPUs! These are the most advanced GPU machines on the market currently. AceCloud  offers access to A100 (80 GB) GPUs for a variety of workloads at very reasonable prices.  

3. Workload distribution –

In the modern digital ecosystem, distributed computing plays a significant role in synchronizing various workloads, while also promoting uninterrupted processor availability and benchmarking. Automating data transfer and computation among the pool of connected devices, be they CPUs or GPUs, within a distributed setup advances productivity and minimizes operational overhead waiting for device availability. 

4. Scalability –

Scalability is of utmost importance for enterprises and business ideas to flourish. With the right mindset, automatable technologies and modular planning, it is easy for enterprises to expand the logical workflow. 

Image-based surveillance systems, financial automation systems, IoT sensors for fire/ smoke/ fault detection, conversational AI chatbots, etc. especially presuppose tremendous processing power availability for expansion. Cloud GPUs can play a significant role here, efficiently scaling up or down depending on enterprise requirements, establishment of new production facilities or onboarding of new clients. 

GPU-powered Process Automation: Some Use Cases –

Manufacturing, healthcare, banking and financial services sector, autonomous vehicles are among some industries that are already utilizing GPU-accelerated AI systems for automation. Common applications include:

1. Automated video surveillance:

Many premises, especially airports and metro stations, have begun advanced CCTV systems that run with AI/ ML algorithms for visual identification, people counter, automated license plate identification, incident analysis, etc.  

2. Automation in healthcare:

Many healthcare facilities have begun experimenting with GPU-supported AI-based patient monitoring systems. Such systems deploy facial and movement recognition technology and behavior predictive algorithms that trigger real-time alerts. 

Other enterprising healthcare firms have begun experimenting with GPU-accelerated automated medical imaging to generate preliminary diagnoses from visual pathology reports (X-rays, ultrasounds, electrocardiographs, CT scans, etc.).  

3. Robotic manufacturing:

Employing automated robotic arms in industrial-scale manufacturing facilities to carry heavy loads or undertake repetitive tasks in the production line. These machines depend on AI-based computer vision, optical inspection and objection detection systems while performing pre-configured manufacturing tasks. Training such optical identification and locomotion-related algorithms is possible, but exceedingly cumbersome, with CPUs only.  

4. Autonomous vehicles:

Autonomous vehicle development has been growing by leaps and bounds. These vehicles use multiple powerful sensors, computer vision and object detection technologies to comprehend their environments. Their autopilot systems must simultaneously process the massive amounts of data being generated from these sensors and modify course accordingly in response to any change in their neighborhood. 

AI/ ML models are rigorously trained for autonomous vehicle navigation, since even the slightest error may lead to terrible fatalities. Autonomous vehicles come equipped with in-built GPUs for navigation and course correction, and electric carmaker Tesla also even deploys GPUs in its backseat entertainment systems!  

5. Transportation and logistics:

AI/ ML-based algorithms have become popular in analyzing and predicting traffic movement and people gathering in different areas, assessing accident damage for insurance disbursal, measuring vehicle speed, determining traffic/ accident-prone areas, facilitating automated toll collection, and so on. It uses the same technology that Google Maps and cab-hailing apps like Uber utilize. Based on these assessments, transportation companies can make informed decisions for efficient logistics routing. 

However, training-testing such algorithms and running prediction and inference ops require extensive computation. GPUs are eminently well-placed, given their SIMD architecture, to execute such compute-heavy analytics and predictions. 

Nvidia is also experimenting with the Smart Cities concept, deploying GPUs for guiding civic authorities with traffic movements, geolocation, sanitation and disease control, etc. 

6. Screening and monitoring:  

Airlines, railways and transportation authorities are adopting GPU-powered AI-automated baggage screening techniques with sensors and advanced vision cameras. Another application of these technologies was automating body temperature checks for airline/ railway passengers during the peak of Covid-19 pandemic.  

7. Industrial fault detection: 

A calibration fault or mechanical defect in an assembly machine or robotic arm can cause the entire production line to malfunction or not comply with the requisite design standards. Some machine faults can also damage manufactured goods in mass production. Many industries incorporate AI systems in conjunction with IoT approach, generating continuous reports and logs and permitting rectification where needed. TTRI’s Smart textile inspection is one such example. 

“Digital Twins”

The concept of digital twins has become a buzzword across industries, especially those incorporating automation in the workflows. The term represents the digital replica of a physical object, service, process, manufacturing unit, or environment. These digital replicas are simulated to appear and behave almost identical to their real-world counterpart. 

Digital twins allow enterprises to experiment with their products, subject them to multiple iterations of testing, simulate performance under different circumstances, explore the challenges of scaling up, and collect data against various scenarios. The entire process of simulating digital twins – from collecting data logs, packaging the data in comprehensible form, running analytics and predicting performance and constraints – can be seamlessly automated. 

Though it is the next big thing in computational engineering and automation, deploying digital twins requires not only considerable knowledge of data structures and computer modelling, but also substantial processing power at hand to simulate these digital replicas, comprehend several use cases, perform real-time analytics and extract insights. Pre-built frameworks for computer vision, natural language processing and ML model training can lighten a developer’s burden and expenditure, but not entirely overcome it.  

GPUs are optimum for powering automation and digital twin research. Nvidia’s A100 GPU can outperform the most advanced CPU by 237 and 30 times respectively in data center inference and image recognition tests. Moreover, AI/ ML model training on GPUs can take advantage of DevOps support from various pre-designed libraries and frameworks like PyTorch, Modulus and TensorFlow, and also integrate containerization and container orchestration capabilities. 

Subscribing to Cloud GPU services in a pay-as-you-go model alleviates many of the financial, cybersecurity and infra management constraints involved with process automation as well. 

Conclusion –

The 4th industrial revolution is irreversibly underway and the march of technology and process automation will continue unabated. AI/ ML-led manufacturing, automation and computational engineering are revolutionizing the world beyond imagination, and every other day brings news of AI-induced breakthroughs in different industries. More and more enterprises and start-ups are raking in the moolah churning out automation approaches, algorithms and technologies.  

Anyone with even the barest of foresight into the future of industry can predict that AI will eventually perform the majority of repetitive/ monotonous/ dangerous tasks. And the human overlords of these AI systems will redirect their attentions towards planning, supervision, course redirection and other more productive work. However, such tremendous AI/ ML and Big Data-driven ecosystems will never even be imaginable without GPUs breathing life into computation, simulation and analytics technologies.