Improving computational performance of AI requires upskilling of professionals in Embedded/VLSI area

By Prof. Satya Sudhakar Yedlapalli, Associate Professor, BITS Pilani Work Integrated Learning Programmes (WILP) division

Artificial intelligence (AI) has evolved significantly since 1950, and has been leading to disruptive transformations and innovations in multiple fields, and even more so in recent years. Gone are the days when electrical and electronic systems relied solely on manual control and traditional programming methods.
With the advent of AI, these systems have become self-learning, adaptive, and capable of making autonomous decisions and optimising their performance in real-time. Unsurprisingly, AI-powered systems are transforming multiple key sectors, such as health care, automation, education, transportation, and manufacturing.

The importance of VLSI’s integration with AI
Implementing AI systems or applications requires intensive computational processors and low-cost power to deploy algorithms. Here, Very Large Scale Integration (VLSI) and embedded system design play a critical role. VLSI design involves the creation and miniaturisation of complex circuits, such as processors, memory circuits, and more recently, customized hardware for AI applications. On the other hand, embedded systems are computing systems for dedicated or specific functionalities, such as smart agriculture or industrial automation. The integration of VLSI with AI has the potential to revolutionise various sectors by enabling faster, more power-efficient, and customised hardware for AI applications.

Some of the key applications of this effective integration are:

Sustainable energy generation and conservation: AI has remarkably impacted electric grids, i.e., power generation, distribution, and management. By analyzing vast amounts of historical data, including energy consumption patterns, weather conditions, and demand forecasts, AI systems can dynamically adjust power distribution, optimise load balancing, and detect and prevent potential faults or outages.

This level of decision-making and intelligence not only enhances the reliability and stability of the grid but also promotes energy conservation and cost savings, thereby significantly preventing energy leakage and saving costs. Integrating AI technologies with renewable systems, such as solar and wind farms, leads to accurate prediction and management of energy generation. This enables better grid integration, maximises energy capture, and minimises wastage, accelerating the transition to a greener and more sustainable future.

Aiding semiconductor ecosystem and design of advanced chips: AI has profoundly impacted
the semiconductor manufacturing process and the design of advanced chips. Designing semiconductor chips is a complex process, involving optimisation of layouts and architectures to meet timing, area, and power constraints. AI-based tools can optimise the placement of transistors and other components to enhance the chip’s efficiency and functionality. Further, functional verification and debugging can also be automated using AI-based tools.

Efficient communication systems for better network performance: AI-based solutions are applied in designing and deploying communication systems to significantly enhance network performance and thereby the overall user experience. Dynamic allocation of resources, such as power and bandwidth, can be done efficiently by AI algorithms, which leads to improved spectral efficiency, reduced interference, and power consumption.

Intelligent beam forming using AI algorithms enables wireless systems to focus their power and frequency band for specific users or devices. This helps in providing a better coverage area and improves the capacity of the network. Further, some of the other specific applications of AI in telecommunication technology are i) Network Self-healing, ii) Interference Management, and iii) Quality of Service (QoS) Management.

Empowering IoT with human-gifted intelligence:
Combining AI and the Internet of Things (IoT) enables intelligence, automation, and decision-making capabilities to connected devices and systems. AI-powered smart house applications use IoT devices, such as thermostats, LED lights, home appliances, gas sensors, and surveillance cameras, to create a connected and automated living environment. For example, depending on the number of people in the living area, the cooling parameters of the Heating, Ventilation, and Air Conditioning (HVAC) unit can be controlled automatically.

IoT and AI are already being used for smart city initiatives, such as traffic management, public safety, and optimised energy usage. Smart streetlights can adjust their intensity based on lighting conditions and traffic/pedestrian movements. Other potential benefits of the application of AI in IoT are data analytics, predictive maintenance, automation, context-aware IoT, natural language processing, and autonomous vehicles.

Enhancing capabilities and functionalities of unmanned vehicles and drones: AI algorithms along with sensing technologies will enable drones to navigate in complex environments for applications, such as delivery and logistics. In smart agriculture applications, AI drones can also analyze the crop’s health and make real-time decisions for applying fertilizer, irrigation, etc. This significantly reduces the cost, of manual intervention, and thereby improves the yield. Further AI- Drones can be used in the defense sector, environment monitoring, search and rescue, infrastructure inspection, and swarm intelligence.

Providing innovative solutions to unique challenges in the healthcare sector: Novel AI algorithms can analyze different kinds of medical images, such as X-rays, MRIs, and CT scans with high accuracy to identify abnormalities. This will assist radiologists/doctors in quick diagnosis, which leads to precise treatment plans for patients. Historical data of the patients can also be analyzed by AI algorithms to predict disease progression and potential complications. This allows doctors to take proactive measures in a timely and efficient manner. The wearable devices can be augmented with historical data for real-time monitoring of patients. Rapid advancements in wireless communication & robotics technology and complemented with AI algorithms enable complex surgeries with greater precision and minimal invasiveness.

Upskilling of working professionals in the Embedded/VLSI area is critical for future success
From optimizing power distribution to revolutionising circuit design and manufacturing, AI technologies are certainly reshaping several industries, improving efficiencies, and driving innovation. The future of electrical and electronics engineering is undoubtedly intertwined with the transformative potential of AI.

As AI continues to evolve, ethical considerations must be at the forefront of its implementation. Accordingly, engineers and researchers need to address concerns related to data privacy, security, and the potential impact on the workforce as well. Design of area-optimised and low-power hardware for AI applications too necessitates the need for strong skills in VLSI & embedded domains. In conclusion, the integration of AI in the field of Electrical and Electronics Engineering has opened up a world of possibilities; so, working professionals need to upskill and reskill themselves accordingly to be able to address these possibilities in an effective and secure manner.