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
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The top 10 results have been unveiled in the first-of-its-kind COVID-19 Lung CT Lesion Segmentation Grand Challenge, a groundbreaking research competition focused on developing artificial intelligence (AI) models to help in the visualization and measurement of COVID specific lesions in the lungs of infected patients, potentially facilitating to more timely and patient-specific medical interventions.
Attracting more than 1,000 global participants, the competition was presented by the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children s National Hospital in collaboration with leading AI technology company NVIDIA and the National Institutes of Health (NIH). The competition s AI models utilized a multi-institutional, multi-national data set provided by public datasets from The Cancer Imaging Archive (National Cancer Institute), NIH and the University of Arkansas, that originated from patients of different ages, genders and with variable disease severity. NVID