Neuromorphic chips are the electronic chips inspired from human brains that are made out of million of neurons. The system is amalgamation of numerous artificial neural network chips which is known as neuromorphic architecture. The neuromorphic computing is executed on hardware with the help of transistors, a threshold switches and oxide based memristors. Neuromorphic chips are the amalgamation of memristors and transistors deployed over a silicon fabrication chip, which assists to lessen memory consumption as it implements the data successively.
According to a new market report published by Transparency Market Research “Neuromorphic Chip Market – Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2015 – 2023” the neuromorphic chip market was valued at US$396.1 mn in 2014, which is expected to reach US$1,801.9 mn by 2023, growing at a CAGR of 19.1% from 2015 to 2023.
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Logistics, ‘aerospace and defense’ and automotive are some of the sectors where neuromorphic chips are used for image recognition. Growing demand of artificial intelligence is also one of the major drivers for the neuromorphic chip market. Artificial intelligence is a machine learning technology that provides the skill to computers to learn with partial programming. This involves the advancement of computer programs which are competent of updating themselves when exposed to real time data. The artificial intelligence implements neuromorphic chips which help to carry out different operations in different ways. This means the artificial intelligence is likely to perform particular task in different ways instead of following one particular process. Thus evolution of artificial intelligence is also expected to increase the application scopes of neuromorphic chips during the forecast period.
Complexities in hardware designing of neuromorphic chips can be identified as one of the major restraining factors in this market. The complicated neuromorphic synapses are difficult to implement in overall established hardware chip design. The reason for this complexity is the intricate algorithm and space that is required to be implemented in the structured hardware. Another major complexity is the limitation in memory capacity of neuromorphic chips. With the technological advancements, impact of these restraining factors is expected to decrease during the forecast period.
Implementation of neuromorphic chips in new applications includes logistics and entertainment can be identified as major opportunity for the market. These chips are likely to be used in industrial and service robotics used in logistics to operate independently without human guidance.