Table of Contents
Introduction
The global neuromorphic computing market is expected to experience significant growth in the coming decade. In 2023, it was valued at USD 5.1 billion and is predicted to reach USD 29.2 billion by 2032, which represents a CAGR of 22% during the forecast period from 2023 to 2032. Neuromorphic computing is modeled after the structure and function of the human brain and seeks to transform the way computers process information, making them more efficient and capable of handling complex tasks like image and signal processing.
One of the key drivers of this market’s growth is the increasing application of deep learning, with sectors such as consumer electronics and automotive leading the demand for neuromorphic chips. The image processing segment, in particular, dominates market applications due to its extensive use across various industries, including healthcare and automotive, for tasks such as medical imaging and autonomous driving. The signal processing segment is also expected to see significant growth, largely due to its applications in speech and audio processing, illustrating the versatile potential of neuromorphic computing technologies.
North America is the leading market share holder, thanks to digital maturity and significant investments such as the 2022 Chips and Science Act, which allocated USD 39.0 billion to the U.S. semiconductor industry.
Recent developments in the field of neuromorphic computing highlight the industry’s commitment to innovation and collaboration. In January 2023, IBM launched an energy-efficient AI chip designed to support a wide range of model types while maintaining leading-edge power efficiency. This chip’s technology allows for scalable application, making it suitable for commercial use, particularly in enhancing cloud security and privacy through closer-to-edge data training.
Another significant development occurred in October 2022 when Intel entered a three-year agreement with Sandia National Laboratories. This collaboration is focused on neuromorphic computing’s potential for addressing large-scale computational problems. Part of the agreement includes exploring Intel’s next-generation neuromorphic architecture and providing Intel’s largest neuromorphic research system, which may exceed more than 1 billion neurons in computational capacity.
Key Takeaways
- The global neuromorphic computing market was valued at USD 5.1 billion in 2023 and is projected to reach USD 29.2 billion by 2032.
- This market is exhibiting a robust CAGR of 22% during the forecast period.
- In 2022, the hardware segment accounted for 65% of the revenue share in the neuromorphic computing market.
- Edge computing dominated the deployment segment with a market share of 67% in 2022, offering cost-saving benefits by offloading processing tasks to local devices.
- Image processing led the application segment with a revenue share of 47% in 2022, driven by increased adoption in sectors such as healthcare and automotive.
- The consumer electronics sector is expected to dominate the market with a revenue share of 57% throughout the forecast period.
- North America is projected to hold the largest market share of 40.5% in the neuromorphic computing market during the forecast period.
- The market is highly competitive, with key players including Intel Corporation, Qualcomm Inc., IBM Corporation, Samsung Electronics Co. Ltd., and others.
Neuromorphic Computing Statistics
- Consumer electronics, which include gadgets like smartphones and laptops, lead in using neuromorphic technologies, holding almost half (48%) of the entire market. This shows their crucial role in improving devices we use daily by making them smarter and more efficient.
- Different neuromorphic systems come with various configurations of neurons and synapses per core, affecting their capabilities and applications:
- DYNAPs and µBrain models each provide 256 neurons per core, with DYNAPs having 16K and µBrain 64K synapses per core, designed for single-chip systems.
- BrainScaleS ups the ante with 512 neurons per core and a massive 128K synapses, all fitted onto a board that accommodates four chips.
- SpiNNaker significantly increases neuron count to 36K per core and 2.8 million synapses, spread over 352 chips on a single board.
- Neurogrid boasts 65K neurons per core and 8 million synapses, organized over 56 chips per board.
For high-capacity systems, Loihi offers 130K neurons and 130 million synapses per core, with a 16-chip board setup. - TrueNorth stands out with 1 million neurons and 256 million synapses per core, integrated into a 4096-chip board configuration.
- A significant development by MIT in 2016 introduced a chip that is ten times more efficient than a mobile GPU, which can significantly boost AI processing on mobile devices by allowing local computation rather than relying on cloud-based processing.
- In a notable advancement in December 2022, Intel unveiled an AI neuromorphic technology claiming to be 1,000 times faster than traditional CPUs and GPUs, while being more energy-efficient, marking a significant leap forward in computing speed and power consumption.
Use Cases
Neuromorphic computing, a field that mimics the structure and function of the human brain, is making strides across various applications. Here’s an in-depth look at key use cases that showcase the potential of this technology:
- Image and Video Recognition: Neuromorphic systems excel in recognizing patterns and objects within images and videos, crucial for surveillance, autonomous driving, and medical diagnostics. Their ability to process data in real-time is particularly valuable for self-driving cars that need to make swift decisions.
- Speech Recognition: These systems can enhance voice-controlled assistants and transcription services by understanding diverse accents, dialects, and languages. They are also adept at improving speech recognition accuracy in noisy environments, broadening their usability across different settings.
- Autonomous Systems and Robotics: Neuromorphic computing can significantly improve the control mechanisms of robots and other autonomous systems, allowing them to interact with their surroundings more naturally. This capability is applicable in a variety of environments, from industrial settings to domestic and healthcare applications.
- Computer to Brain Interfaces: Advancements in neuromorphic computing are paving the way for sophisticated brain-computer interfaces. These interfaces could potentially allow humans to control devices directly with their thoughts, opening up possibilities in medical prosthetics, gaming, and more.
- Energy Efficiency: One of the standout features of neuromorphic systems is their energy efficiency, making them ideal for battery-powered and remote applications. This trait is particularly beneficial for IoT devices and applications in challenging environments like space exploration or deep-sea research.
Intel’s Loihi 2 chip and the Lava software framework are at the forefront of neuromorphic computing research. Loihi 2’s enhancements include up to 10x faster processing, 60x more inter-chip bandwidth, and support for up to 1 million neurons. These developments are set to drive the future of adaptive AI by optimizing hardware for next-generation AI software.
Moreover, research teams are exploring novel approaches like using magnetic disks for data processing, indicating the diverse technological paths being investigated within the field. Such advancements highlight the role of neuromorphic computing in solving complex problems, such as traffic optimization, by analyzing vast data sets for patterns that conventional computer architectures struggle with.
As the technology progresses, we’re likely to see neuromorphic computing become increasingly integral to developing intelligent, energy-efficient devices and systems capable of autonomous decision-making. This exciting area of research holds promise for transforming various sectors, including healthcare, automotive, and beyond.
Recent Developments
- Intel’s Loihi 2 and Lava Software Framework: Intel introduced Loihi 2, their second-generation neuromorphic research chip, which promises up to 10 times faster processing capability and up to 60 times more inter-chip bandwidth. It also supports up to 1 million neurons with 15 times greater resource density than its predecessor. The launch of Loihi 2 is complemented by Lava, an open-source software framework designed to advance the adoption of neuromorphic computing solutions.
- Intel and AMD Partnership: Intel has collaborated with AMD, a leading technology development company, to enhance neuromorphic chip development. This partnership aims to leverage innovative solutions for developing neuromorphic chips. Additionally, Intel initiated a Parallel Computing Lab focusing on applications such as big data, machine learning, and neuromorphic computing.
- IBM’s Neuromorphic Chip Developments: IBM continues to be a key player in neuromorphic computing. In 2011, IBM unveiled TrueNorth, a brain-like chip with 4,096 processor cores, emulating 1 million human neurons and 256 million synapses. More recently, in January 2021, IBM launched an energy-efficient AI chip built with 7nm technology, supporting various model types and aimed at enhancing power efficiency for commercial applications.
- Collaborations and Research Initiatives: Intel has also been part of several collaborations aimed at advancing neuromorphic computing. One notable project, supported by Intel and Accenture, is led by the Neuro-Biomorphic Engineering Lab at the Open University of Israel, focusing on assisting wheelchair-bound pediatric patients. Additionally, research initiatives with the National University of Singapore and Cornell University have explored applications of neuromorphic computing in robotics and sensory systems, demonstrating the technology’s potential in healthcare and AI applications.
Key Players Analysis
Qualcomm Inc.
Qualcomm is advancing in the neuromorphic computing field with its Zeroth processors. These processors are designed to mimic the human brain and nervous system, enabling devices to have embedded cognition driven by brain-inspired computing. Qualcomm’s aim with Zeroth is to achieve biologically inspired learning, making it a significant player in the neuromorphic computing industry. For more detailed information, you can visit Qualcomm’s official announcement on their website.
Intel Corporation
Intel Corporation is at the forefront of neuromorphic computing with its second-generation research chip, Loihi 2, and the Lava software framework. These advancements promise up to 10 times faster processing, 60 times more inter-chip bandwidth, and support for up to 1 million neurons, indicating significant strides towards mimicking the human brain’s efficiency and learning capabilities. This technology is expected to revolutionize AI by making devices more intelligent and energy-efficient, opening up new possibilities in robotics, healthcare, and large-scale AI applications.
IBM Corporation
IBM is focusing on neuromorphic computing to tackle next-generation AI’s challenges, offering a brain-inspired, energy-efficient computing paradigm. Their research, conducted in Zurich, aims to optimize learning and computing efficiency by employing tactics inspired by biological systems. This approach is pivotal for advancing AI capabilities, emphasizing sustainability and computational efficiency.
Samsung Electronics Co. Ltd.
Samsung Electronics Co., Ltd. is actively exploring the frontier of neuromorphic computing, aiming to develop chips that mimic the human brain’s structure and functionality. In collaboration with Harvard University, Samsung has proposed an innovative approach to reverse engineer the brain onto a memory chip, aiming to create neuromorphic chips that exhibit low power consumption, quick learning, and adaptability to environments, traits that current technology has not achieved. This collaboration resulted in a perspective paper published in Nature Electronics, detailing a ‘copy and paste’ method to map the brain’s neuronal connections onto a high-density three-dimensional network of solid-state memories.
Moreover, at the Samsung Advanced Institute of Technology (SAIT), research into neuromorphic processors is a key focus. SAIT’s efforts center around developing brain-like processors through studying near/in-memory computing, asynchronous spiking neural networks, and novel synaptic memories. These endeavors are part of Samsung’s broader initiative to harness neuromorphic engineering for creating processors that emulate the dynamic learning capability and energy efficiency of the human brain.
In a significant stride toward next-generation AI semiconductors, Samsung demonstrated the world’s first in-memory computing based on MRAM (Magnetoresistive Random Access Memory). This breakthrough, detailed in a paper published in Nature, showcases Samsung’s leadership in merging memory and system semiconductor technologies for AI chips. By implementing a ‘resistance sum’ in-memory computing architecture, Samsung’s MRAM array chip achieved high accuracy in AI tasks, such as classifying handwritten digits and detecting faces, marking a milestone in the development of low-power AI semiconductor chips.
These developments reflect Samsung’s commitment to leading in the field of neuromorphic computing, contributing to the advancement of next-generation computing and AI semiconductors.
Conclusion
The neuromorphic computing market is at a pivotal stage, with substantial growth projected over the next decade. Driven by advancements in technology, increasing demand across key sectors, and strategic industry collaborations, the market is set to redefine computing paradigms, making technology more efficient, scalable, and capable of addressing complex computational tasks.
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