Accelerating Deep Learning with Mixed Precision Arithmetic with Greg Diamos - TWiML Talk #97
Published:Jan 17, 2018 22:19
•1 min read
•Practical AI
Analysis
This article discusses an interview with Greg Diamos, a senior computer systems researcher at Baidu, focusing on accelerating deep learning training. The core topic revolves around using mixed 16-bit and 32-bit floating-point arithmetic to improve efficiency. The conversation touches upon systems-level thinking for scaling and accelerating deep learning. The article also promotes the RE•WORK Deep Learning Summit, highlighting upcoming events and speakers. It provides a discount code for registration, indicating a promotional aspect alongside the technical discussion. The focus is on practical applications and advancements in AI chip technology.
Key Takeaways
Reference
“Greg’s talk focused on some work his team was involved in that accelerates deep learning training by using mixed 16-bit and 32-bit floating point arithmetic.”