Why Should you take AI Hardware Courses
Understanding AI hardware is imperative in the realm of artificial intelligence as it plays a pivotal role in optimizing system performance. This knowledge is crucial for selecting and configuring hardware components that enhance the speed and efficiency of AI algorithms, ensuring their optimal execution which can be obtained from the courses mentioned below.
Moreover, it facilitates informed decision-making in terms of cost-effective infrastructure, considering factors such as energy consumption and overall operational expenses.
Performance Optimization:
Learning AI hardware is vital for optimizing the performance of AI systems. This knowledge helps in choosing and configuring hardware components to enhance the speed and efficiency of AI algorithms, ensuring optimal execution.
Cost-Effective Solutions:
AI hardware knowledge enables informed decisions regarding cost-effective infrastructure. By understanding the hardware requirements of AI workloads, individuals and organizations can manage costs efficiently, considering factors like energy consumption and overall operational expenses.
Innovation and Customization:
Proficiency in AI hardware empowers professionals to innovate and design custom solutions tailored to specific AI tasks. This customization can lead to breakthroughs in performance, supporting the development of more powerful and specialized AI applications.
Scalability Planning:
AI applications are growing in complexity and scale, making knowledge of AI hardware crucial for scalability. Professionals can design systems that handle increasing workloads effectively by understanding hardware architecture and implementing optimization strategies.
Interdisciplinary Collaboration:
Collaboration between hardware engineers and AI researchers is facilitated by a shared understanding of AI hardware. Bridging the gap between these disciplines is essential for developing advanced AI technologies, as hardware considerations directly impact the feasibility and performance of AI algorithms.
Read More: 10 Free Online AI Courses From Top Companies
An Analogy
Consider asking a friend to guess a book’s name. If you provide vague hints like the book’s color, subject, and first chapter, their answer might be uncertain due to the open-ended nature of the clues. However, if you include more details like the author’s name and the published year along with the earlier hints, their prediction becomes more accurate and quicker. In machine learning, these hints are analogous to parameters. The more parameters fed into the model during training, the more accurate the predictions.
Latest trends in AI hardware
Current large language models demand extensive parameters for training, necessitating high-bandwidth memory and parallel processing capabilities. Traditional CPUs operate sequentially, leading to prolonged training times. GPUs, with parallel execution, significantly reduce compute time, making them ideal for AI model training. GPT-3.5 was trained on Microsoft’s Azure AI supercomputing infrastructure. The supercomputer employed 10,000 Nvidia A100 GPUs.
The number of GPUs required to training depends on the number of parameters in the ML model.
Another approach towards AI hardware is non-Von Neuman architecture. This architecture merges memory and processor. Thus, eliminating data movement time and reducing the need for high-bandwidth memory. Neuromorphic computing is the most famous approach towards non-Von Neumann architecture. It is inspired by the human brain’s neural networks. The neurons act as the compute units and the synapses (neuron junctions) act as storage units.
Read More: Master AI and ML: A Deep Dive into 15 FREE Courses Tailored for Every Skill Level
5 NPTEL courses for AI hardware
With the advancement of AI there is a dire need in the market to produce processors which can handle tasks parallelly or can emulate the Artificial neural networks. Keeping this in mind we have curated a few NPTEL courses which can be used to learn the concepts of the hardware required for AI.
1. Parallel computer architecture by Prof. Hemangee Kapoor | IIT Guwahati
Enrollment status – Live, upcoming January semester
Pre-requisites: Basic computer architecture and organization
This course will introduce learners to the field of parallel computing architectures. It will discuss in-depth shared memory management for parallel architectures. As the demand for AI increases, the demand for AI hardware will also increase.
To make a career in AI hardware, understanding of parallel computing architecture is of paramount importance. One major application (as mentioned in the course) where parallel computing is employed is weather forecasting. This is because it involves computing many aspects and interaction of many components to decide the final outcome.
Course link – Parallel Computer Architecture – Course (nptel.ac.in)
2. GPU architectures and programming, by Prof. Soumyajit Dey | IIT Kharagpur
Enrollment status – Live, YouTube records also available
Pre-requisites – Programming & Data structures, Digital logic, Computer architecture
We are sure you might have realized the importance of GPUs in training large machine learning models with substantial parameter counts. This course covers basics of conventional GPU architecture and then explores the CUDA cores. CUDA cores are specialized processing units within NVIDIA graphics processing units (GPUs) that are designed for general-purpose computing in addition to their primary function of rendering graphics.
Before the all this, the course also gives an overview of basic RISC pipeline and Cache memory. The course might come handy for those wishing to get placed at Nvidia.
Course link – GPU Architectures and Programming – Course (nptel.ac.in)
YouTube records – GPU Architectures and Programming- YouTube
3. Introduction to Computational Neuroscience, By Prof. V Srinivasa Chakravarthy | Centre for Neuroscience, IISc Bangalore
Enrollment status – YouTube records available
Pre-requisites – 1st year college Mathematics & Biology.
Before diving into the world of Neuromorphic chips, a detailed understanding of how our brain functions is very important. This course by IISc first creates an understanding of the neural activity in the brain and then moves on to the mathematical modelling of the neuron structure.
While delving into the working of neural system Prof. Srinivasa explains essential topics like spiking neurons, synapses, inter-neuron communication and shows these concepts practically in a tutorial using the NEURON Simulator (lecture 19, www.neuron.yale.edu/neuron/). In the modelling of the neuron structure the course briefly touches deep neural network models of sensory systems and Hopfield networks (network of multiple neural networks). This course is best suited for students interested in neural and cognitive sciences and AI.
The knowledge gained can then be leveraged to understanding neuromorphic architecture. Some chips which employ neuromorphic architecture are Intel’s Loihi and IBM’s NorthPole.
YouTube records – Introduction to Computational Neuroscience – YouTube
4. Applied Accelerated Artificial Intelligence, jointly delivered by IIT Pallakad, Nvidia & others
Enrollment status – Closed, Youtube records available
Pre-requisites – Computer architecture, High performance computing, ML & Deep Learning
This course covers the fundamentals of the compute capabilities and the system software required to implement AI based solutions for industrial use cases. The course is structured around the use cases in smart cities and healthcare. It discusses end to end deployments with demonstrations.
Initially, it briefs the learner about the AI system hardware requirements such as CPU, RAM, GPUs, AI accelerators and also gives the highlights of the operating system required to run such large-scale systems. As we go into the weeks 3 & 4 we get to learn different frameworks that are required for AI deployment such as PyTorch and TensorFlow.
The course is jointly delivered by IIT Palakkad, Nvidia, Kumoh National Institute of Technology, South Korea, KLE Tech university. The technical partners for the course are Nvidia and National Supercomputing mission (initiative under Govt. Of India) by C-DAC.
Course link – Applied Accelerated Artificial Intelligence – Course (nptel.ac.in)
Youtube link – Applied Accelerated Artificial Intelligence – YouTube
5. Multi core computer architecture by Prof. John Jose, IIT Guwahati
Enrollment status – Closed, runs autumn semester
Pre-requisites – Basic understanding of digital logic, microprocessors, Computer architecture
Today even handheld devices like a smart phone house multiple processor cores in a single chip. The core counts are ever increasing from 8 to 10 in smartphones to over 100s in supercomputers. This course will introduce learners to multi-core computer architectures.
The course first briefs learners about fundamentals of basic computer organization such as basics of processors (CISC/RISC ISA, multi/hyper threading, GPU arch.) and memory (cache memory from basics & DRAM organization). After covering the basics of processors and memory, the learners are then introduced to multi-core systems, spanning roughly three weeks of teaching.
The course is designed in such a way that it can be taken by undergraduate students in the 2nd year of their BTech program. At the end of the course learners will learn computer architecture with an emphasis on system design, performance and analysis.
Indian government wants more people to take up architectural research. This course addresses to this need and enables exploration of future directions in computer architecture research.
Course link – Multi-Core Computer Architecture – Course (nptel.ac.in)
Youtube records – Multi-Core Computer Architecture – YouTube
Conclusion
These AI hardware Courses reaps best results when one has registered for the exam certificate. The exam registeration costs a decent amount to students. However, this loss of payment inculcates sincerity and consistency.
If you plan to do the above-mentioned courses, make sure you do it formally with a test registeration whenever the course goes live.
