Jim Keller: Moore's Law, Microprocessors, and First Principles | Lex Fridman Podcast #70 | Summary and Q&A

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February 5, 2020
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Lex Fridman Podcast
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Jim Keller: Moore's Law, Microprocessors, and First Principles | Lex Fridman Podcast #70

TL;DR

Jim Keller discusses the similarities and differences between the human brain and computers, the basics of building a computer from scratch, the future of Moore's Law, and the potential of exponential growth in technology.

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Questions & Answers

Q: How do the human brain and computers differ in terms of memory and computation?

Jim Keller explains that computers have separate memory and computation units, whereas the human brain combines memory and computation in a mesh-like structure. He notes that while both rely on distributed information processing, the mathematical understanding of neural networks is still limited.

Q: How do you build a computer from scratch?

Keller describes the process of building a computer, starting with atoms and assembling them into transistors, logic gates, and functional units. He emphasizes the importance of abstraction layers and teamwork in the construction process.

Q: Will Moore's Law continue despite predictions of its demise?

Keller believes that Moore's Law will continue, although the rate of transistor shrinking may change. He explains that there are numerous innovations involved in shrinking transistors, and the cascade of diminishing returns curves supports ongoing exponential growth in technology.

Q: What are the key challenges in designing computers for the future?

Keller highlights the challenge of balancing increasing computational power with limited human capabilities, such as team size and individual intelligence. He also mentions the need to adapt design strategies and architecture to accommodate new innovations and behaviors in software.

Q: How do the human brain and computers differ in terms of memory and computation?

Jim Keller explains that computers have separate memory and computation units, whereas the human brain combines memory and computation in a mesh-like structure. He notes that while both rely on distributed information processing, the mathematical understanding of neural networks is still limited.

More Insights

  • The human brain and computers have similarities in terms of memory and computation, but they differ in the complexity and organization of their information processing.

  • Building a computer involves multiple layers of abstraction and teamwork, from designing transistors to creating functional units and software.

  • Instruction sets and parallelism are essential elements in computer architecture, with GPUs focusing on parallel processing and finding dependency graphs between instructions.

  • Moore's Law, which states the doubling of transistor count or computing performance every 2 years, continues to hold true with ongoing innovations and advancements in shrinking transistors.

  • The future of technology holds the potential for significant exponential growth, with the ability to increase computational power by a factor of a million or more, leading to transformative changes in various industries.

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Summary

This conversation is with Jim Keller, a legendary microprocessor engineer who has worked at AMD, Apple, Tesla, and now Intel. He is known for his work on several microarchitectures, including AMD K7, K8, K12, and Xen, as well as Apple A4 and A5 processors. He co-authored the specification for the x86-64 instruction set and hyper-transport interconnect. In this conversation, Keller discusses the differences and similarities between the human brain and computers, the basics of building a computer from scratch, the importance of different layers of abstraction in computer design, the challenges in designing for Moore's Law, and the future of computing.

Questions & Answers

Q: What are the differences and similarities between the human brain and a computer?

Jim Keller explains that it is hard to compare the human brain and computers since our understanding of the human brain is limited. Computers have memory and computation, while the human brain has a combination of local and global connections storing information in a distributed fashion. However, both use similar concepts of neurons and neural networks.

Q: How do you build a computer from scratch?

Keller provides a breakdown of the abstraction layers involved in computer engineering, starting from atoms and building transistors, logic gates, and functional units. Computers are then built using processing elements and software, with different disciplines and considerations for each layer.

Q: What is the most important layer in the design and architecture of computers?

Keller states that he doesn't have a favorite layer and finds the entire stack of abstraction layers fascinating. He believes that the composition of a team with different skill sets is essential for successful computer design.

Q: What is the relationship between instruction sets and computer architecture?

Instruction sets define the basic operations that a computer can perform, such as load, store, multiply, add, subtract, and conditional branch. Architecture describes how these instructions are encoded and executed. Keller explains that instruction sets are relatively stable, but the way instructions are executed has evolved, allowing for more parallelism and optimizing performance.

Q: How are branches predicted in modern computers, and what happens when a prediction is wrong?

Keller explains the various methods used to predict branches and how each prediction is based on a history and counter method. In modern computers, the prediction accuracy for branches is typically high, around 90-99%. When a prediction is wrong, the pipeline is flushed, resulting in a performance cost. However, modern computers also have mechanisms to retain already-calculated results, minimizing the impact of branch mispredictions.

Q: What is the difference between found parallelism and given parallelism, and how do GPUs utilize parallelism?

Keller explains that found parallelism is when a program has inherent parallelism and can execute instructions in any order without affecting the outcome. On the other hand, given parallelism is when a large number of parallel tasks are present, such as in GPUs. GPUs can efficiently process multiple simple programs on pixels while maintaining parallelism, as the order of computation does not affect the final outcome.

Q: What are the challenges in designing for Moore's Law and the constant increase in transistor count?

Keller highlights the need to constantly adapt and change the design approach as transistor counts increase. He emphasizes the importance of designing with the expectation of more transistors every few years, as not doing so would drown design teams with complexity. Additionally, he argues that innovations in various areas, including equipment, optics, chemistry, and material science, contribute to the ongoing progress of Moore's Law.

Q: What changes when more transistors are added to a computer design?

Keller discusses the need to divide and conquer the design process as computers get bigger, using abstraction layers and optimization techniques. However, he also notes the challenges posed by the limitations of human intelligence and team sizes, which impact the design process.

Q: Is Moore's Law still alive, and how does it impact the future of computing?

Keller expresses his belief that Moore's Law is not dead and highlights how the continuous progress in transistor technology and other innovations contribute to the growth of computing performance. He notes that the shrinking of transistors is expected to continue in the next 10-20 years, leading to new possibilities in computer design and architecture.

Q: What are the possibilities for advancement in computing performance beyond just shrinking transistors?

Keller discusses how mathematical operations in computing have evolved from simple equations to more complex computations, such as convolutional neural networks. He believes that advancements will continue, with computation and data sets supporting higher-level mathematical concepts. The future may involve dealing with large data sets as topology problems, with computers leveraging both computation and data understanding to optimize performance.

Takeaways

Jim Keller provides insights into the differences and similarities between the human brain and computers, the basics of computer architecture, the challenges and future of Moore's Law, and the possibilities for advancement in computing performance. He emphasizes the importance of understanding different layers of abstraction and the need for continuous innovation in order to push the boundaries of computing.

Summary & Key Takeaways

  • Jim Keller highlights the differences and similarities between the human brain and computers, noting that while computations in the brain are more complex, both rely on memory and computation.

  • He explains the process of building a computer from scratch, starting with atoms and transistors and assembling them into functional units, highlighting the importance of abstraction layers and teamwork.

  • Keller discusses the significance of instruction sets in computer architecture and the shift towards parallelism in GPUs, emphasizing the need for computational efficiency and performance optimization.

  • He expresses optimism about the future of Moore's Law and the potential for ongoing innovation and advancements in technology, particularly in shrinking transistor size and exploring new computation methods.

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