Smart Data Webinar: Emerging Hardware Choices for Modern AI Data Management
Leading edge AI applications have always been resource-intensive and known for stretching the limits of conventional (von Neumann architecture) computer performance. Specialized hardware, purpose built to optimize AI applications, is not new. In fact, it should be no surprise that the very first .com internet domain was registered to Symbolics – a company that built the Lisp Machine, a dedicated AI workstation – in 1985. In the last three decades, of course, the performance of conventional computers has improved dramatically with advances in chip density (Moore’s Law) leading to faster processor speeds, memory speeds, and massively parallel architectures. And yet, some applications – like machine vision for real time video analysis and deep machine learning – always need more power.
Participants in this webinar will learn the fundamentals of the three hardware approaches that are receiving significant investments and demonstrating significant promise for AI applications.
- Neuromorphic/neurosynaptic architectures (brain-inspired hardware)
- GPUs (graphics processing units, optimized for AI algorithms), and
- Quantum computers (based on principles and properties of quantum-mechanics rather than binary logic).
Note – This webinar requires no previous knowledge of hardware or computer architectures.
Leading edge AI applications have always been resource-intensive and known for stretching the limits of conventional (von Neumann architecture) computer performance. Specialized hardware, purpose built to optimize AI applications, is not new. In fact, it should be no surprise that the very first .com internet domain was registered to Symbolics – a company that built the Lisp Machine, a dedicated AI workstation – in 1985. In the last three decades, of course, the performance of conventional computers has improved dramatically with advances in chip density (Moore’s Law) leading to faster processor speeds, memory speeds, and massively parallel architectures. And yet, some applications – like machine vision for real time video analysis and deep machine learning – always need more power.
Participants in this webinar will learn the fundamentals of the three hardware approaches that are receiving significant investments and demonstrating significant promise for AI applications.
– Neuromorphic/neurosynaptic architectures (brain-inspired hardware)
– GPUs (graphics processing units, optimized for AI algorithms), and
– Quantum computers (based on principles and properties of quantum-mechanics rather than binary logic).
Note – This webinar requires no previous knowledge of hardware or computer architectures.
