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Advanced AI Accelerators and Processors with Andrew Feldman of Cerebras Systems
Manage episode 366800412 series 3011550
On this episode, we’re joined by Andrew Feldman, Founder and CEO of Cerebras Systems. Andrew and the Cerebras team are responsible for building the largest-ever computer chip and the fastest AI-specific processor in the industry.
We discuss:
- The advantages of using large chips for AI work.
- Cerebras Systems’ process for building chips optimized for AI.
- Why traditional GPUs aren’t the optimal machines for AI work.
- Why efficiently distributing computing resources is a significant challenge for AI work.
- How much faster Cerebras Systems’ machines are than other processors on the market.
- Reasons why some ML-specific chip companies fail and what Cerebras does differently.
- Unique challenges for chip makers and hardware companies.
- Cooling and heat-transfer techniques for Cerebras machines.
- How Cerebras approaches building chips that will fit the needs of customers for years to come.
- Why the strategic vision for what data to collect for ML needs more discussion.
Resources:
Andrew Feldman - https://www.linkedin.com/in/andrewdfeldman/
Cerebras Systems - https://www.linkedin.com/company/cerebras-systems/
Cerebras Systems | Website - https://www.cerebras.net/
Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.
#OCR #DeepLearning #AI #Modeling #ML
131 قسمت
Manage episode 366800412 series 3011550
On this episode, we’re joined by Andrew Feldman, Founder and CEO of Cerebras Systems. Andrew and the Cerebras team are responsible for building the largest-ever computer chip and the fastest AI-specific processor in the industry.
We discuss:
- The advantages of using large chips for AI work.
- Cerebras Systems’ process for building chips optimized for AI.
- Why traditional GPUs aren’t the optimal machines for AI work.
- Why efficiently distributing computing resources is a significant challenge for AI work.
- How much faster Cerebras Systems’ machines are than other processors on the market.
- Reasons why some ML-specific chip companies fail and what Cerebras does differently.
- Unique challenges for chip makers and hardware companies.
- Cooling and heat-transfer techniques for Cerebras machines.
- How Cerebras approaches building chips that will fit the needs of customers for years to come.
- Why the strategic vision for what data to collect for ML needs more discussion.
Resources:
Andrew Feldman - https://www.linkedin.com/in/andrewdfeldman/
Cerebras Systems - https://www.linkedin.com/company/cerebras-systems/
Cerebras Systems | Website - https://www.cerebras.net/
Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.
#OCR #DeepLearning #AI #Modeling #ML
131 قسمت
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