May 2019 | 58 pages
Find out the applications, innovation landscape and future impact of neuromorphic chipsets
Impact of Neuromorphic chipset adoption in IoT, Automotive, Finance, Cyber-security, Aerospace, and Medical systems
Active projects by 19 entities, including IBM, Qualcomm, Intel, Brainchip, HumanBrain Project, DARPA and HRL Laboratories, among others
Analysis of patent data from 1999 to 2018
Top assignees of neuromorphic hardware patents
Technology focus areas of leading patent assignees
Geographic distribution of patent assignees
Emerging players and active universities
Key collaborations
Neuromorphic chips are microprocessors with higher computational power than conventional chips. Inspired by the human brain, these chips integrate memory and processing units in the same location, enabling high connectivity, parallelism, and real-time computing. neuromorphic chips emulate the human brain’s neural activities through artificial synapses, neurons, and axons. Currently at an early stage of development, neuromorphic chips are expected to give a boost to future AI applications by enabling high-accuracy perception and interpretation of data and prediction of future events.
Synapses receive signals from other neurons as voltage spikes
Function in a smooth analog pattern of voltage
Transmits voltage spikes to other neurons
mulated synapeses that communicate by means of spikes
Integrate incoming signals for parallelism
Wire is an emulated axon that mimics an axon’s operations
Low power consumption
The human brain performs complex computations on a small power budget of about 20 W of power compared with the supercomputers that require kW or MW power for AI applications
Fault tolerant
Neuromorphic chips continue to operate even after the failure of a few components of the chip. This reduces the cost of production of the chip due to lower fabrication tolerances
Stochastic operation
Current AI chipsets are ordered, and operate in a calculated manner. However, neuromorphic chips are stochastic in nature because of which they can be used for all applications
Pattern recognition
The low-power pattern recognition of neuromorphic hardware helps to classify objects, make predictions, or anticipate conditions. It is also possible to understand the context of such patterns by using neuromorphic chips
Faster computation
The inherent massive parallelism and low latency factor of neuromorphic chips make it possible to perform complex computations faster. Neuromorphic chips use less training data compared with other intelligent chipsets
Scalability
Neuromorphic architectures can be employed in edge applications and can also be scaled up for server applications
The concept of emulating neurons on a chip could enhance complex operations to make business decisions secure and cost-effective. Parallel connected neurons can boost AI verticals compared with the conventional processing systems. Non-stop learning and pattern recognition using this human brain architecture can help compute signals and data in the form of visual, speech, olfactory, etc., to perform real-time operations as well as predict outcomes based on detected patterns. Neuromorphic chipsets can also enhance performance owing to their low-power consumption to process AI algorithms.
Neuromorphic Chipsets: Introduction to the Concept and Architecture
Neuromorphic Chipsets: Advantages
Industry Adoption of Neuromorphic Chipsets
Neuromorphic Chipsets: Active Projects
Neuromorphic Chipsets: Patent Analysis