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Hardware for Deep Learning

Dive into the intricacies of Deep Learning hardware through extensive resources. This deep exploration provides an in-depth comprehension of hardware solutions for Deep Learning, equipping you with advanced knowledge and practical insights in this domain. For those new to this journey, there's no requirement for hardware investments.

hardware for Deep learning

Google Colab offers cost-effective Deep Learning and ML with a free NVIDIA T4 GPU. As your projects become more computation-intensive, you can explore renting online GPUs. Alternatively, consider Google Colab Pro, which offers access to NVIDIA V100, A100 GPUs.



  • GPUs: Explained (opens in a new tab): The video provides an overview of GPUs, their role in accelerating various computing tasks, including deep learning, explains the architecture of GPUs, their parallel processing capabilities, and their advantages in handling complex mathematical operations required for deep learning models.

  • How are memories stored in neural networks? (opens in a new tab): Explores neural network memory storage through Hopfield Network, encompassing associative memory for retrieval from incomplete/noisy inputs, enhanced with animations.

  • CPU vs GPU vs TPU vs DPU vs QPU (opens in a new tab): By Fireship, the video evaluates CPU, GPU, TPU, DPU, and QPU performance while delving into their respective strengths, weaknesses, and operational mechanisms.

  • A Full Hardware Guide to Deep Learning (opens in a new tab): By Tim Dettmers provides a comprehensive guide to building a high-performance deep learning system. ordered by mistake severity, with the most common mistakes listed first. The author emphasizes the importance of using a GPU for deep learning and provides recommendations for specific models. discusses the role of PCI-Express lanes in deep learning performance and concludes that they have almost no effect on performance.

  • AI’s Hardware Problem (opens in a new tab) by Asianometry, explains Von Neumann architecture, memory scalability, the concept of "computer in memory," practical limitations, RAM, circuitry, and related topics.

  • Putting the “You” in CPU (opens in a new tab): By Lexi Mattick & Hack Club, aims to bridge the gap between low-level knowledge and understanding how programs execute on CPUs. It explores topics such as syscalls, program execution, multitasking, provides in-depth explanations and interactive visualizations to help users grasp the inner workings of CPUs and how they process instructions and data.


  • Hardware for Machine Learning (opens in a new tab): The EE 290 by University of California, Berkeley, covers current topics of research interest in electrical engineering, focuses on hardware for ML and is designed as a seminar-style class, with students expected to present, discuss, and interact with research papers also includes readings, labs, projects, and course participation, with the option for extra credit through contributions to Piazza, Gemmini/Chipyard code.



  • Computer Architecture: A Quantitative (5th Edn) (opens in a new tab) is a widely acclaimed book by John L Hennessy & David A. Patterson, recommended by top institutions mainly for electrical engineering students, offers in-depth knowledge of hardware, making it an ideal choice for those seeking a deep understanding of computer architecture.

    If you've already explored the preceding resources, this book is optional. It's merely an extra resource for hardware enthusiasts.