Neuromorphic Chip Market Size (2024 - 2029)

Neuromorphic Chip Market Analysis

The Neuromorphic Chip Market size is estimated at USD 0.16 billion in 2024, and is expected to reach USD 5.83 billion by 2029, growing at a CAGR of 104.70% during the forecast period (2024-2029).

• The increasing use of biometrics and in-speech recognition drives the demand for neuromorphic chips in smartphones. These chips are used to process audio data in the cloud and then return it to the phone. In addition, Artificial Intelligence (AI) requires more computing power, but low-energy neuromorphic computing could significantly push applications that run presently in the cloud to run directly in the smartphone in the future without substantially draining the phone battery.

• Neuromorphic is a specific brain-inspired ASIC that implements the Spiked Neural Networks (SNNs). On average, it has an object that can reach a massively parallel brain processing ability in tens of watts. The memory and the processing units are in single abstraction (in-memory computing).

• This leads to the advantage of dynamic, self-programmable behavior in complex environments. Instead of traditional bit-precise computing, neuromorphic hardware leads to the probabilistic models of simple, reliable, robust, and data-efficient computing as the brain's highly stochastic nature. Neuromorphic hardware certainly suits more cognitive applications than precise computing.

• During the next decade, neuromorphic computing will transform the nature and functionalities of a wide range of scientific and non-scientific applications. Some of them include mobile applications that are increasingly demanding powerful processing capacities and abilities.

• The design of neuromorphic chips follows the goal of modeling parts of the biological nervous system. The aim is to reproduce its computational functionality, especially its ability to efficiently solve cognitive and perceptual tasks. Achieving this requires modeling networks of sufficient complexity regarding the number of neurons and synaptic connections. The brain and its ability to learn and adapt to specific problems are still subject to basic neuroscientific research.

• The telltale spike in energy demand resulting from neuromorphic computing devices using potentially very small quantities of electricity makes hardware attacks much easier to identify. This increase would be visible through side-channel monitoring. Neuromorphic device designers may use brain functionality as a blueprint to create computing systems that use 3D nanostructures, biomaterials, redox memristors, magnetic neural network crossbar arrays, and other technologies.

• The COVID-19 pandemic had a favorable influence on the medical business market. Several market leaders, including IBM, Hewlett Packard, and Qualcomm, pushed their neuromorphic computing solutions into several hospitals and clinics worldwide. Their technologies' computational skills were able to reduce various difficulties inside a normal hospital ecosystem. The pandemic kept the capital equipment sector humming with a strong demand for next-generation electronics.

Neuromorphic Chip Industry Overview

Neuromorphic chips are digitally-processed analog chips with a series of networks similar to human brain networks. These chips contain millions of neurons and synapses to augment self-intelligence, irrespective of pre-installed codes in normal chips. Neuromorphic chips, as a special kind of chip, can manipulate data received through sensors. For the study, chips that adopt the SNN approach have been considered part of the scope. The market studied tracks deep learning hardware and neuromorphic chips as separate markets.

The Neuromorphic Chip Market is segmented by End-User Industry (Financial Services & Cybersecurity, Automotive (ADAS/Autonomous Vehicles), Industrial (IoT Ecosystem, Surveillance, and Robotics), Consumer Electronics), Geography (North America, Europe, Asia Pacific, Rest of the World). The market sizes and forecasts are provided in terms of value (USD) for all the above segments.