The authors present a summary of their major contributions to the field of deep learning and its applications.
In this paper, they address concerns over the energy consumption, speed, and feasibility of large-scale deep learning on optical platforms.
They demonstrate that linear operations in Transformers could be accurately conducted on real optical hardware, despite errors and noise.
They also show that optical energy scaling and noise relate to Transformer construction and performance.
Their major contributions include:• They created scaling rules for the performance and energy costs of optical Transformers vs. the model size and optical energy use.•
They experimentally showed that linear operations inTransform could be accurate on real Optical hardware, regardless of error and noise.
• They want to know more about the relationship between scale and energy in Transformers
👋 Feeling the vibes?
Keep the good energy going by checking out my Amazon affiliate link for some cool finds! 🛍️
If not, consider contributing to my caffeine supply at Buy Me a Coffee ☕️.
Your clicks = cosmic support for more awesome content! 🚀🌈
Leave a Reply