Super natural unit 2
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The document discusses natural disasters and weather phenomena such as tornadoes, hailstorms, landslides, floods, and volcanic eruptions. It then explains how to form the past simple tense in English, noting that regular verbs take the "-ed" ending while irregular verbs must be memorized using the second column of an irregular verb list. Regular past simple verbs provided include played, decided, arrived, and accepted.
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âHow Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
âHow Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the âHow Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Visionâ tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his companyâs pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.Â
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