Super Data Science: ML & AI Podcast with Jon Krohn

691: A.I. Accelerators: Hardware Specialized for Deep Learning

Jun 27, 2023

Join Ron Diamant, Senior Principal Engineer at AWS, as he delves into the world of AI accelerators, discussing GPUs vs CPUs, chip design, AWS's accelerators Trainium and Inferentia, model optimizations, chip production process, AWS Neuron SDK, and his journey into chip design.

01:34:34

Episode guests

Ron Diamant

AI Summary

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Episode notes

Podcast summary created with Snipd AI

Quick takeaways

Designing chips for AI involves predicting future needs and adapting to evolving technology.

Efficiency in hardware design balances optimization for computations with flexibility for diverse operations.

Challenges in scaling up models necessitate utilizing parallelism methods for efficient compute distribution.

Deep dives

Anticipating the Future in Chip Design

When architecting a new chip, the long-term vision involves predicting the needs for devices in the coming years by considering the potential changes in the workload as technology evolves. An approach focused on decomposing workloads into generalized primitives allows for future adaptability and efficiency, enabling customers to customize workload implementations. The concept of inverting difficult questions helps in identifying elements that will remain constant over time, such as algebraic operations and different data types, ensuring the chip's design aligns with enduring demands and future requirements.

Intro

3min

The Role of GPUs in the AI Revolution

11min

Exploring Differences in Deep Learning Accelerators

4min

Specialized Hardware Accelerators for Deep Learning Workloads

13min

Exploring the Significance of AI Accelerators in Deep Learning

2min

Specialized Hardware Chips for Deep Learning and AI Applications

14min

Exploring the Future of AI Companionship and Open-Source Architectures in Deep Learning

2min

Specialized Chips for Deep Learning Models

28min

Specialized Hardware Accelerators and Software Integration for Deep Learning

16min

10.

A Deep Dive into AI Accelerators and Chip Design

4min

GPUs vs CPUs, chip design and the importance of chips in AI research: This highly technical episode is for anyone who wants to learn what goes into chip development and how to get into the competitive industry of accelerator design. With advice from expert guest Ron Diamant, Senior Principal Engineer at AWS, you’ll get a breakdown of the need-to-know technical terms, what chip engineers need to think about during the design phase and what the future holds for processing hardware.

This episode is brought to you by Posit, the open-source data science company, by the AWS Insiders Podcast, and by WithFeeling.ai, the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• What CPUs and GPUs are [05:29]
• The differences between accelerators used for deep learning [14:31]
• Trainium and Inferentia: AWS's A.I. Accelerators [22:10]
• If model optimizations will lead to lower demand for hardware to process them [43:14]
• How a chip designer goes about production [48:34]
• Breaking down the technical terminology for chips (accelerator interconnect, dynamic execution, collective communications) [55:29]
• The importance of AWS Neuron, a software development kit [1:15:42]
• How Ron got his foot in the door with chip design [1:26:40]

Additional materials: www.superdatascience.com/691

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