Executive Course on Accelerators for Deep Learning By IIT Roorkee
Computing systems have fueled the growth of AI. Improvements in deep-learning algorithms have inevitably gone hand-in-hand with the improvements in the hardware-accelerators. Our ability to train increasingly-complex AI models and achieve low-power, real-time inference depends on the capabilities of computing systems.
In recent years, the metrics used for optimizing and evaluating AI algorithms are diversifying: along with accuracy, there is increasing emphasis on the metrics such as energy efficiency and model size. Given this, researchers working on deep-learning can no longer afford to ignore the computing-system. Rather, the knowledge of potential and limitations of computing-system can provide invaluable guidance to them in designing the most efficient and accurate algorithms.
This course aims to inform students, practitioners and researchers in deep-learning algorithms about the potential and limitations of various processor architectures for accelerating the deep learning algorithms. At the same time, it seeks to motivate and even challenge the engineers and professionals in the architecture domain to optimize the processors according to the needs of deep-learning algorithms.
This course discusses acceleration of AI algorithms on various computing systems such as FPGAs, mobile GPUs, smartphones, ASICs (e.g., such as Google's TPU) and CPUs. We primarily focus on CNNs and will also include recurrent neural networks. Apart from performance and energy metrics, this course will also discuss hardware reliability and security issues/techniques for deep-learning algorithms/accelerators. We will also draw from recent research papers to showcase the state-of-art in these fields.
This course is at the intersection of deep learning algorithms and computer architecture, and chip-design, and thus, is expected to be beneficial for a broad range of audience.
Upon successfully completing the course, you will get the certificate from IIT Roorkee which you can use for progressing in your career and finding better opportunities.
The candidate should have an idea of what is deep learning, especially the basics of CNNs and RNNs. Background in computer architecture or embedded-system is preferred, although not mandatory.
A brief outline of the Instructor-Led Training course: 36+ Hours of Training 60 days of cloud lab Real-world projects Automated assessments and quizzes Lifetime access to the course content Verified certificate from IIT Roorkee 24x7 support to answer your queries and doubts