iii. Growth Opportunities Fuel the Growth Pipeline Engine
Chapter 1 - Executive Summary
1.2 Research Methodology
1.4 Summary of the Key Findings
1.5 Impact of Enabling Technologies in Neuromorphic Computing Based Applications
Chapter 2 - Technology Landscape of Neuromorphic Computing
2.1 Neuromorphic Computing Technology Landscape - An Overview
2.2 R&D Trends: Key R&D Areas Strengthening Commercialization Potential of Invasive BCI Solutions
2.3 Key Technology Attributes to Focus on for Future Product Development
2.4 R&D Drivers: Government Supporting R&D Initiatives is a Major Driving Force for Advancements in Neuromorphic Computing
2.5 Market Drivers Accelerating the Growth of Neuromorphic Computing
2.6 Distributing Large Amount of Synapses on a Single Compact Chipset is a Major Bottleneck Hindering Wide-Scale Advancements in Neuromorphic Computing Space
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Conventional artificial intelligence (AI)-enabled processors are based on rule-based algorithms and are optimized for applications, such as real-time monitoring and reporting. However, with increasing demand for fully autonomous solutions, there is a need for perceptive solutions capable of making probabilistic decisions, replicating the human brain.
With technology advancements in chipset architecture and algorithms, neuromorphic chipsets are processing powerhouses that are logically analogous to neurons that exist in biological human brains and efficiently perform complicated decision-making tasks.
Neuromorphic computing holds the potential to emerge as a vital breakthrough in applications across the healthcare, manufacturing, and aerospace & defense sectors. With increased funding and related support from government bodies and Tier-1 OEMs, neuromorphic solutions are poised to witness exponential traction from venture capitalists and governme