Neuromorphic computing is anticipated to gain momentum owing to the increasing demand for artificial intelligence. Artificial intelligence is extensively used in nonlinear controls & robotics, language processing, translation & chatterbots, and computer vision & image processing among others.
Neuromorphic computing has emerged in the recent years as a complementary architecture to von Neumann systems. These neuromorphic architectures are notable for being parallel and highly connected, requiring low-power, and collocating memory and processing. Neuromorphic computers have the potential to perform complex calculations faster, more power-efficiently, and on a smaller footprint than the traditional von Neumann architectures. These characteristics provide compelling reasons for developing hardware that employs neuromorphic architectures.
Machine learning provides the second important reason for strong interest in neuromorphic computing. The approach shows promise in improving the overall learning performance for certain tasks. This moves away from hardware benefits to understanding potential application benefits of neuromorphic computing, with the promise of developing algorithms that are capable of on-line, real-time learning, similar to what is done in biological brains. Neuromorphic architectures appear to be the most appropriate platform for implementing machine learning algorithms in the future.
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Major Key Players
IBM Corp. (U.S.),
Hewlett Packard Enterprise (U.S.),
Samsung Group (South Korea),
Intel Corp. (U.S.),
HRL Laboratories, LLC (U.S.),
General Vision Inc. (U.S.),
Applied Brain Research Inc. (U.S.),
BrainChip Holdings Ltd. (U.S.)
The Global Neuromorphic Computing Market is expected to reach USD 225 Million by 2023
The IBM TrueNorth chip, developed under DARPA's SyNAPSE programme, is based upon distributed digital neural models and aimed at real-time cognitive applications. The key component is a 5.4 million transistor CMOS chip that incorporates 4096 neurosynaptic cores. TrueNorth chips can be connected together to form larger systems, and a 16-chip circuit board has been developed that incorporates 16 million neurons and 4 billion synapses. Designed as an "application delivery" platform to address problems ranging from vision to audition and multi-sensory fusion, TrueNorth offers power-efficient real-time processing of high-dimensional, noisy, and sensory data. For example, it can run applications such as real-time object recognition at remarkably low power levels.
Militaries today are coping up with an exponential increase in the amount of data from a wide variety of sensors. Unprecedented data collection has severely strained the limited available bandwidth for military use. The data needs to be processed, as close to the sensor as possible before further transmission therefore sequential computational techniques with their large size and power requirements are not very efficient in this regard. Neuromorphic chips can carry out this parallel task much more efficiently. The U.S. Air Force has projected a requirement to develop a new class of advanced, wide field of view (WFOV) imaging sensors that sample the radiation field in multiple modes: spectral, temporal, polarization, and detailed object shape.
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The North American region account for a major share of the global market. The strong presence of the neuromorphic chip manufacturers in this region can be attributed as a major reason for this share. Furthermore, the industry is predicted to gain momentum in the Asia Pacific and South American regions, owing to the increasing demand for automation in countries such as China, India, South Korea, and Brazil. The European region is expected to present significant opportunities for industry expansion as the expenses of neuromorphic projects and R&D activities are high.
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