Measuring the Algorithmic Efficiency of Neural Networks
📖 Description
Measuring the Algorithmic Ef?ciency of Neural NetworksDanny Hernandez?[email address redacted] B. [email address redacted] factors drive the advance of AI: algorithmic innovation, data, and the amount ofcompute available for training. Algorithmic progress has traditionally been more dif?cultto quantify than compute and data. In this work, we argue that algorithmic progress hasan aspect that is both straightforward to measure and interesting: reductions over timein the compute needed to reach past capabilities. We show that the number of ?oating-point operations required to train a classi?er to AlexNet-level performance on ImageNethas decreased by a factor of 44x between 2012 and 2019. This corresponds to algorithmicef?ciency doubling every 16 months over a period of 7 years. Notably, this outpaces theoriginal Moore?s law rate of improvement in hardware ef?ciency (11x over this period).We observe that hardware and algorithmic ef?ciency gains multiply and can be on a si...
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Not verified in gameWhich variables this event was proposed to move, and in which direction. The magnitudes are held in the corpus but are not shown here, because they have not been verified against the shipped game. They come from pdoom-data. They describe what an event was proposed to do, not what the shipped game does with it. Most events in the corpus are flavour: they are shown for colour and do not move any game variable. Only a small minority reach the systems below, and several of the variables listed here are not read by the game at all yet. Treat this table as a design proposal under review, not as a measurement of play. Corrections and arguments are welcome — the suggestion links at the foot of this page go straight to the data repo.
| Variable | Direction | Condition |
|---|---|---|
| Research | proposed: up | Always |
| Papers | proposed: up | Always |
| Vibey Doom | proposed: up | Always |
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