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The Transformative Impact of Artificial Intelligence on Hardware Development: Its Applications, Need for Redesigning Chips, Market Growth and Who is the Leading Chipmaker for AI

Artificial Intelligence is making some remarkable progress in almost every domain possible. With the increasing popularity and advancements, AI is transforming how we work and operate. From the task of language understanding in Natural Language Processing and Natural Language Understanding to major developments in hardware, AI is booming and evolving at a fast pace. It has provided wings to creativity and better analytic and decision-making abilities and has become a key technology in the software, hardware, and language industries, offering innovative solutions to complex problems.

Why Integrate AI with Hardware?

A massive amount of data is generated every single day. Organizations are deluged with data, be it scientific data, medical data, demographic data, financial data, or even marketing data. AI systems that have been developed to consume and analyze that data require more efficient and robust hardware. Almost all hardware companies are switching to integrating AI with hardware and developing new devices and architectures to support the incredible processing power AI needs to make use of its full potential. 

How is AI being used in hardware to create smarter devices?

  1. Smart Sensors: AI-powered sensors are being actively used to collect and analyze large amounts of data in real time. With the help of these sensors, making accurate predictions and better decision-making have become possible. Some examples are that in healthcare, sensors are used to collect patient data, analyze it for future health risks, and to alert healthcare providers of potential issues before they become more severe. In agriculture, AI sensors predict soil quality and moisture levels to inform farmers about the best crop yield time.
  1. Specialized AI Chips: Companies are designing specialized AI chips, such as GPUs and TPUs, which are optimized to perform the matrix calculations that are fundamental to many AI algorithms. These chips help accelerate the training and inference process for AI models.
  1. Edge Computing: These devices integrate with AI to perform tasks locally without relying on cloud-based services. This concept is used in low-latency devices like self-driving cars, drones, and robots. By performing AI tasks locally, edge computing devices reduce the amount of data that needs to be transmitted over the network and thus improve performance. 
  1. Robotics: Robots integrated with AI algorithms perform complex tasks with high accuracy. AI teaches robots to analyze spatial relationships, computer vision, motion control, intelligent decision-making, and work on unseen data.
  1. Autonomous vehicles: Autonomous vehicles use AI-based object detection algorithms to collect data, analyze objects, and make controlled decisions while on the road. These features enable intelligent machines to solve problems in advance by predicting future events by quickly processing data. Features like Autopilot mode, radar detectors, and sensors in self-driving cars are all because of AI.

Increasing Demand for Computation Power in AI Hardware and current solutions

With the growing usage of AI hardware, it needs more computation power. New hardware specifically designed for AI is required to accelerate the training and performance of neural networks and reduce their power consumption. New capabilities like more computational power and cost-efficiency, Cloud and Edge computing, faster insights, and new materials like better computing chips and their new architecture are required. Some of the current hardware solutions for AI acceleration include – the Tensor Processing Unit, an AI accelerator application-specific integrated circuit (ASIC) developed by Google, Nervana Neural Network Processor-I 1000, produced by Intel, EyeQ, part of system-on-chip (SoC) devices designed by Mobileye, Epiphany V, 1,024-core processor chip by Adapteva and Myriad 2, a vision processor unit (VPU) system-on-a-chip (SoC) by Movidus. 

Why is Redesigning Chips Crucial for AI’s Impact on Hardware?

Traditional computer chips, or central processing units (CPUs), are not well-optimized for AI workloads. They lead to high energy consumption and declining performance. New hardware designs are strongly in need so that they can handle the unique demands of neural networks. Specialized chips with a new design need to be developed, which are user-friendly, durable, reprogrammable, and efficient. The design of these specialized chips requires a deep understanding of the underlying algorithms and architectures of neural networks. This involves developing new types of transistors, memory structures and interconnects that can handle the unique demands of neural networks. 

Though GPUs are the current best hardware solutions for AI, future hardware architectures need to provide four properties to overtake GPUs. The first property is user-friendliness so that hardware and software are able to execute the languages and frameworks that data scientists use, such as TensorFlow and Pytorch. The second property is durability which ensures hardware is future-proof and scalable to deliver high performance across algorithm experimentation, development, and deployment. The third property is dynamism, i.e., the hardware and software should provide support for virtualization, migration, and other aspects of hyper-scale deployment. The fourth and final property is that the hardware solution should be competitive in performance and power efficiency. 

What is currently happening in the AI Hardware Market?

The global artificial intelligence (AI) hardware market is experiencing significant growth due to an increase in the number of internet users and the adoption of industry 4.0, which has led to a rise in demand for AI hardware systems. The growth in big data and significant improvements in commercial aspects of AI are also contributing to the market’s growth. The market is being driven by industries like IT, automotive, healthcare, and manufacturing. 

The global AI hardware market is segmented into three types: Processors, Memory, and Networks. Processors account for the largest market share and are expected to grow at a CAGR of 35.15% over the forecast period. Memory is required for dynamic random-access memory (DRAM) to store input data and weight model parameters. The network enables real-time conversations between networks and ensures the quality of service. According to research, the AI Hardware market is primarily being run by the companies like Intel Corporation, Dell Technologies Inc, International Business Machines Corporation, Hewlett Packard Enterprise Development LP, and Rockwell Automation, Inc.

How is Nvidia Emerging as Leading Chipmaker, and what is its role in the popular ChatGPT?

Nvidia has successfully positioned itself as a major supplier of technology to tech firms. The surge of interest in AI has led to Nvidia reporting better-than-expected earnings and sales projections, causing its shares to rise by around 14%. NVIDIA’s revenue has mostly been derived from three main regions – the U.S., Taiwan, and China. From the year 2021 to 2023, the firm saw revenues come less from China and more from the U.S.

With a market value of over $580 billion, Nvidia controls around 80% of the graphics processing units (GPUs) market. GPUs provide the computing power which is necessary for major services, including Microsoft-backed OpenAI’s popular chatbot, ChatGPT. This famous large language model already has over a million users and has risen among all verticals. Since it requires GPU to carry the AI workloads and feed and perform various data sources and calculations simultaneously, NVIDIA plays a major role in this famous chatbot. 


In conclusion, the impact of AI on hardware has been significant. It has driven significant innovation in the hardware space, leading to more powerful and specialized hardware solutions optimized for AI workloads. This has enabled more accurate, efficient, and cost-effective AI models, paving the way for new AI-driven applications and services.

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  • https://www.verifiedmarketresearch.com/product/global-artificial-intelligence-ai-hardware-market/
  • https://medium.com/sciforce/ai-hardware-and-the-battle-for-more-computational-power-3272045160a6
  • https://www.computer.org/publications/tech-news/research/ais-impact-on-hardware
  • https://www.marketbeat.com/originals/could-nvidia-intel-become-the-face-of-americas-semiconductors/
  • https://www.reuters.com/technology/nvidia-results-show-its-growing-lead-ai-chip-race-2023-02-23/

Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.

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