High-Level Project Summary
AI chips are built with specific architecture and have integrated AI acceleration to support deep learning based applications. AI chips help turn data into information and then into knowledge. The worldwide AI industry accounted for $8.02 billion in 2020 and expected to reach $194.9 billion by 2030, growing at a compound annual growth rate (CAGR) of 37.4% from 2021 to 2030.The increasing adoption of AI chips is one of the major factors driving the growth of the market. In computer vision some of the chips support in-vehicle computers to run state of the art AI applications more efficiently. In robotics AI chips are also covering applications of computational imaging in wearable electronics.
Link to Final Project
Link to Project "Demo"
Detailed Project Description
APPLICATIONS:
Natural language processing (NLP)-
✓The use of AI chips for NLP applications has increased due to the raise in demand for chatbots and online channels such as messanger, slack, and others.
✓The use NLP to analyse user messages and conversational logic.
Used for network security across a wide variety of sectors, including automotive, IT, healthcare, and retail.
AI processors with on chip hardware acceleration are designed to help customers achieve business insights at scale across banking, finance, trading, insurance applications and customer interactions.
Space Agency Data
NASA scientists are trying to figure that out by partnering with pioneers in artificial intelligence (AI) — companies such as Intel, IBM and Google — to apply advanced computer algorithms to problems in space science.
Machine learning is a type of AI. It describes the most widely used algorithms and other tools that allow computers to learn from data in order to make predictions and categorize objects much faster and more accurately than a human being can. Consequently, machine learning is widely used to help technology companies recognize faces in photos or predict what movies people would enjoy. But some scientists see applications far beyond Earth.
Hackathon Journey
Deep learning can make the process of collecting, analysing, and interpreting enormous amounts of data faster and easier.AI chips generally contain processor cores as well as several AI optimised cores(depending on the scale of the chip) that are designed to work in harmonic when performing computational tasks. The AI cores are optimised for the demands of heterogeneous enterprises- class AI work loads with low - latency inferencing, due to close integration with the other processor cores , which are designed to handle non-AI applications.
References
The increasing adoption of AI chips is one of the major factors driving the growth of the market. In computer vision some of the chips support in-vehicle computers to run state of the art AI applications more efficiently.AI chips generally contain processor cores as well as several AI optimised cores(depending on the scale of the chip) that are designed to work in harmonic when performing computational tasks. Used for network security across a wide variety of sectors, including automotive, IT, healthcare, and retail.