Nov. 11, 2023, 5:40 p.m. | /u/APaperADay

Machine Learning www.reddit.com

**Report**: [https://epochai.org/blog/trends-in-machine-learning-hardware](https://epochai.org/blog/trends-in-machine-learning-hardware)

**Dataset**: [https://docs.google.com/spreadsheets/d/1NoUOfzmnepzuysr9FFVfF7dp-67OcnUzJO-LxqIPwD0/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1NoUOfzmnepzuysr9FFVfF7dp-67OcnUzJO-LxqIPwD0/edit?usp=sharing)

**Abstract**:

>We analyze recent trends in machine learning hardware performance, focusing on metrics such as computational performance, memory, interconnect bandwidth, price-performance, and energy efficiency across different GPUs and accelerators. The analysis aims to provide a holistic view of ML hardware capability and bottlenecks.

https://preview.redd.it/x47il6669rzb1.png?width=1610&format=png&auto=webp&s=b0bbd98602aa983501c1d675e1aeac093f06fb95

abstract accelerators analysis analyze bandwidth capability computational efficiency energy energy efficiency gpus hardware machine machine learning machinelearning memory metrics ml hardware performance price trends

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