Neuromorphic Chip Market To Expand At A CAGR Of 43.8% From 2022 To 2031

Rise in adoption of neuromorphic chip from industrial sector and increase in demand for neuromorphic chips for Big Data analysis in pharmaceutical and bioinformatics applications are expected to boost the global neuromorphic chip market during the forecast period.

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The neuromorphic chip offers new frontiers for smart sensors and cognitive storage, and recognizes patterns extracted from text, measurement, images, and signals with orders of magnitude less energy and complexity than modern microprocessors. Power-hungry autonomous vehicles, robots, drones, and other self-reliant machines require small and energy-efficient chips, and traditional semiconductor chips are reaching the limits of miniaturization and capacity. Thus, emergence of neuromorphic computing is vital in these sectors. Neuromorphic computing employs new algorithmic approaches to evaluate how the human brain reacts with world to deliver capabilities closer to human cognition. Artificial intelligence involves computer programs that are competent enough to update themselves when being exposed to real-time data.

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Neuromorphic computing uses spiking neural networks (SNN) to emulate the biological spikes to transmit a signal before returning to the silent state. The resulting system is more power-efficient than artificial neural networks used in most AI system presently. This increased efficiency opens up a new possibility of carrying out much more AI processing on smaller low-power devices at the network edge. The neuromorphic chip offers several key advantages, which include low latency, rapid response system, and low consumption of power, higher adaptability, and fast-paced learning. However, slow rate of development and high complexity in the designing hardware may hinder the growth of the market.

Rise in Adoption of Neuromorphic Chip in Industrial Sector

Neuromorphic chips are utilized in a wide range of industries, including automotive, aerospace, medicine, industrial applications, and defense. The industrial sector is presently extensively adopting neuromorphic chips across a wide range of applications, including electronic gadgets, machines, and automated industry controls.

Smartphones, laptops, and tablet computers, which are becoming increasingly popular in the field of consumer electronics, serve as a form of industrial products that can benefit considerably from neuromorphic chips in terms of processing power and application inclusion. Consequently, the industrial sector is estimated to continue to be a major consumer of neuromorphic chips and would have a significant impact on the growth of the global market. The market is also projected to be driven by the rise in the number of applications in medicine, with drug delivery and personal sensing devices being the most likely choices for testing.

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The automotive industry is significantly utilizing neuromorphic chip for premium cars. Autonomous driving needs continued advancements and improvements in AI algorithms for greater efficiency and low power consumption. Neuromorphic chips are more efficient in a noisy environment and self-driving vehicles. Neuromorphic chips offer more elegant and energy-efficient ways to create self-driving cars.

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Rise in Demand for Neuromorphic Chips for Big Data Analysis, Research in Pharmaceutical and Bioinformatics Applications

Deep Artificial Neural Networks (DNNs) offer prominent usefulness in pharmacology and bioinformatics for drug design, virtual screening (VS), Quantitative Structure Activity Relationship (QSAR) research, protein structure prediction and genomics, and data mining. Machine Learning (ML) algorithms have been successfully deployed in a variety of computational tasks for pharmacology and bioinformatics.

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