Global Neuromorphic Computing, AI Hardware And Edge Analytic Market Size By Deployment (Edge Computing, Cloud Computing), By Offering (Hardware, Software), By Application (Image Recognition, Signal Recognition), By Vertical (Industrial, Medical), By Geographic Scope And Forecast

Neuromorphic Computing, AI Hardware And Edge Analytic Market Size And Forecast

Neuromorphic Computing, AI Hardware And Edge Analytic Market size was valued at USD 43.70 Million in 2022 and is projected to reach USD 241.14 Million by 2030, growing at a CAGR of 23.80% from 2023 to 2030.

The expanding need for high-performance Integrated Circuits is a significant factor in the growth of the global neuromorphic computing industry. By processing and storing data on the same chip, neuromorphic devices can significantly reduce the time a typical CPU spends moving data around. The Global Neuromorphic Computing, AI Hardware And Edge Analytic Market report provides a holistic evaluation of the market. The report offers a comprehensive analysis of key segments, trends, drivers, restraints, competitive landscape, and factors playing a substantial role in the market.

Global Neuromorphic Computing, AI Hardware And Edge Analytic Market Definition

Neuromorphic Computing is the latest development in line with technological developments in the field of Artificial Intelligence, with its focus on extending Artificial Intelligence into areas that emulate human cognition, for instance, activities such as autonomous adaptations and interpretations. The outputs of artificial intelligence based on neural networks and algorithms, which lack any human context to the issue statement and are mostly dependent on the trend that a particular data set has seen in the past, have been significantly improved by this technical advancement. This is why the next generation of AI aims to create a system that can deal with unusual circumstances in a way similar to how a human would deal with them. Probabilistic computing and neuromorphic computing, which aim to replicate the neural architecture of the human brain, could work together to handle the uncertainties and complexities of the modern world effectively.

The Spiking Neural Network (SNN), a particular type of neural network, serves as the foundation for neuromorphic computing. An artificial neural network with enough ambition to model its architecture after the network of neurons in the human brain, each of which transmits signals independently of the others and affects the electrical states of the others. The SNN can mimic the adaptability and agility of the human brain due to the way it functions. By constantly adjusting the electrical signal, one of the computational building blocks of an SNN (which is similar to a neuron of a human brain), the SNN can recreate the learning processes of a human brain by encoding the information included within the signals themselves, as well as their timing.

Global Neuromorphic Computing, AI Hardware And Edge Analytic Market Overview

The expanding need for high-performance Integrated Circuits is a significant factor in the growth of the global neuromorphic computing industry. By processing and storing data on the same chip, neuromorphic devices can significantly reduce the time a typical CPU spends moving data around. The time a regular CPU would have needed to shuttle data between a block of memory and the processor handling these memories’ processing tasks is significantly decreased by the ability to combine processing and storage. As a result, the demand for higher-performing ICs for efficient computing is fueling market growth.

To increase productivity and product quality, many sectors must automate their processes using artificial intelligence and machine learning. Numerous businesses use AI extensively, including those in the medical, media, telecom, auto, food, and beverage sectors. Since SNN can make fluid and agile decisions while considering the context of the scenario, it can effectively address the difficulties that these industries frequently face. Combining AI with ML can improve applications’ efficiency, including fraud detection, credit scoring, speech recognition, self-driving cars, image classification, and language translation.

The market is expanding due to the increasing demand for general-purpose humanoid robots with cognitive and cerebral capabilities. The switch from Von Neumann architecture to neuromorphic chips, another market growth driver, is driven by the inherent technological advantages of neuromorphic chips, such as reduced power consumption, higher speed, and optimal memory usage. The global demand for automation has increased due to COVID-19, spurring the expansion of the Neuromorphic Computing, AI Hardware And Edge Analytic Market in the IT and medical sectors. However, it is anticipated that the complexity of algorithms and backend processes may impede market growth. Increasing spending on research and development in the field of neuromorphic computing is expected to fuel market expansion in the forecast period.

Global Neuromorphic Computing, AI Hardware And Edge Analytic Market Segmentation Analysis

The Global Neuromorphic Computing, AI Hardware And Edge Analytic Market is Segmented on the basis of Deployment, Offering, Application, Vertical, and Geography.

Neuromorphic Computing, AI Hardware And Edge Analytic Market, By Deployment

• Edge Computing
• Cloud Computing

Based on Deployment, the market is segmented into Edge Computing and Cloud Computing. Cloud computing is expected to have a wider market presence in the forecast period due to the numerous technological advantages it offers such as a stop platform for securely storing and transporting huge amounts of data for any organization.

Neuromorphic Computing, AI Hardware And Edge Analytic Market, By Offering

• Hardware
• Software

Based on Offering, the market is segmented into Hardware and Software. The software segment is expected to have a larger market share owing to the incremental software needs across various industries such as telecom and media, which is supported by the software applications of Neuromorphic Computing such as real-time data streaming, data modeling, and predictions. The hardware segment is further divided into processors and memory.

Neuromorphic Computing, AI Hardware And Edge Analytic Market, By Application

• Image Processing
• Signal Processing
• Data Processing
• Object Detection
• Others

Based on Application, the market is segmented into Image Processing, Signal Processing, Data Processing, Object Detection, and Others. Image Processing is expected to be in prominence over the forecast period, owing to the advancements in digital cameras and other processing systems.

Neuromorphic Computing, AI Hardware And Edge Analytic Market, By Vertical

• Automotive
• Consumer Electronics
• Aerospace, Military and Defense
• IT and Telecommunication
• Industrial
• Medical
• Others (Smart Infrastructure and Education)

Based on Vertical, the market is segmented into Automotive, Consumer Electronics, Aerospace, Military and Defense, IT and Telecommunication, Industrial, Medical, and Others (Smart Infrastructure and Education). Approximately 30% of the market is expected to be occupied by Aerospace, Military and Defense. This is due to the applications that Neuromorphic Computing can provide in the field of the military such as the secure and speedy transmission of signals containing critical information, resource management, and battlefield surveillance amongst others.

Neuromorphic Computing, AI Hardware And Edge Analytic Market, By Geography

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East and Africa

Based on Regional Analysis, the Neuromorphic Computing, AI Hardware And Edge Analytic Market is classified into North America, Europe, Asia Pacific, Latin America, the Middle East and Africa. North American region is expected to grow at the highest CAGR in the forecast period. It is expected to occupy around 40% of the market in 2021. This can be due to the countries in the North American region, being the leading implementers of a major number of technological advancements and rising R&D investments in the area of Neuromorphic Computing.

Key Players

The “Global Neuromorphic Computing, AI Hardware And Edge Analytic Market” study report will provide valuable insight with an emphasis on the global market including some of the major players such as IBM Corporation, Intel Corporation, Brainchip Holdings Limited, Qualcomm Technologies, HP Enterprise, HRL Laboratories LLC, Flow Neuroscience AB, Innatera Nano systems B.V., Aspinity, Inc., Samsung Electronics Limited, and others are prominent manufacturers operating in the market.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis.

Ace Matrix Analysis

The Ace Matrix provided in the report would help to understand how the major key players involved in this industry are performing as we provide a ranking for these companies based on various factors such as service features & innovations, scalability, innovation of services, industry coverage, industry reach, and growth roadmap. Based on these factors, we rank the companies into four categories as Active, Cutting Edge, Emerging, and Innovators. 

Market Attractiveness

The image of market attractiveness provided would further help to get information about the region that is majorly leading in the Global Neuromorphic Computing, AI Hardware And Edge Analytic Market. We cover the major impacting factors that are responsible for driving the industry growth in the given region.

Porter’s Five Forces

The image provided would further help to get information about Porter’s five forces framework providing a blueprint for understanding the behavior of competitors and a player’s strategic positioning in the respective industry. Porter’s five forces model can be used to assess the competitive landscape in the Neuromorphic Computing, AI Hardware And Edge Analytic Market , gauge the attractiveness of a certain sector, and assess investment possibilities.

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