41–50 of 138 posts
They're not really "grafting" it, they're implementing it as a first-class data type.
Ditto for them calling it "Brain Floating Point" [1]. I mean it appears to be a typical floating-point numeric data type with some of the precision truncated to reduce its cost.
I guess they might be trying to make it sound super-fancy to preempt people seeing it as cheap or/and low-tech?
[1]: https://en.wikipedia.org/wiki/Bfloat16_floating-point_format
English is not my first language. I have never heard the term "Graft", even if I consider myself quite literate in English. So here you go, for everybody else in my situation: Graft, as understood in American English, is a form of political corruption, being the unscrupulous use of a politician's authority for personal gain. Edit: by the way, I really couldn't fit the term with the article. And realized I was probabl…
I am sorry for Intel. Perhaps John Gustafson’s 16 bit posits or unums would have made a better choice.
Why? Google has certainly researched their floats before committing an entire line of silicon chips. It's easy to just enumerate all possible float16 configurations in a simulator to see which one performs best on a wide range of neural network applications. Then pick the best one. Big data driven organizations do this all the time (brute force through an entire line of solutions, pick best results).
It's possible that some other custom format is better in absolute terms for models trained specifically for the custom format. But for the current ecosystem, where models are trained primarily using float32, bfloat16 is a very good choice.
English is not my first language. I have never heard the term "Graft", even if I consider myself quite literate in English. So here you go, for everybody else in my situation: Graft, as understood in American English, is a form of political corruption, being the unscrupulous use of a politician's authority for personal gain. Edit: by the way, I really couldn't fit the term with the article. And realized I was probabl…
English is not my first language. I have never heard the term "Graft", even if I consider myself quite literate in English. So here you go, for everybody else in my situation: Graft, as understood in American English, is a form of political corruption, being the unscrupulous use of a politician's authority for personal gain. Edit: by the way, I really couldn't fit the term with the article. And realized I was probabl…
This is the definition they most likely meant.
They're not really "grafting" it, they're implementing it as a first-class data type.
Definitely feels click-bait-y. Ditto for them calling it "Brain Floating Point" [1]. I mean it appears to be a typical floating-point numeric data type with some of the precision truncated to reduce its cost. I guess they might be trying to make it sound super-fancy to preempt people seeing it as cheap or/and low-tech? [1]: https://en.wikipedia.org/wiki/Bfloat16_floating-point_format
Earlier quoted context omitted.
Yes, there’s a bios setting to control this. It basically under clocks the core while AVX units are under load.
Taking my 7980xe as an example: When it runs non-avx512 loads, I currently have it set to run at 4.1 GHz (all-core). When running avx-512 heavy loads, it instead runs at 3.6 GHz -- and tends to get much hotter (70-80C instead of 50-60C). 3.6 GHz is a mild overclock; Silicon Lottery reports 100% can achieve that speed for avx512 loads.[1] Running programs doing the same thing (eg, Hamiltonian Monte Carlo where the lik…
Earlier quoted context omitted.
Definitely feels click-bait-y. Ditto for them calling it "Brain Floating Point" [1]. I mean it appears to be a typical floating-point numeric data type with some of the precision truncated to reduce its cost. I guess they might be trying to make it sound super-fancy to preempt people seeing it as cheap or/and low-tech? [1]: https://en.wikipedia.org/wiki/Bfloat16_floating-point_format
It's called brain floating point because it was developed by Google's "Google Brain" machine learning project.
Why "graft"? It's not something that's foreign to them. This promises to essentially double the performance of Intel chips on an increasingly important workload, and also simplify the modeling work, because the models don't experience any accuracy drop when simply converted to bfloat16, unlike with quantization, where it's model dependent and finicky AF. I'd much rather do fp16 or bfloat16 at inference time, without…
Intel (for better or worse) takes a very experiment-results-driven approach to choosing which features to implement in hardware. So this result -- that software emulation of a feature works almost as well as a hardware implementation would -- probably makes Intel less likely to implement the feature in hardware.
ARM, AMD, RISCV etc, will probably come to similar conclusions.