Artificial intelligence is currently all over the news, every day, everywhere. In this context, you might have stumbled upon the term TOPS, and you may be wondering what it actually means. Here’s all you need to know about it, in simple terms.
What Does TOPS Stand For?
TOPS stands for Tera Operations Per Second, and it’s a unit of measurement for the performance of a computing system when it’s working with AI. Specifically, it measures the system’s ability to perform trillions of floating-point operations per second.
You’ll notice this unit when manufacturers talk about computers designed for AI. It's used to simplify and specify the performance of the Neural Processing Unit (NPU) in the PC.
While TOPS numbers aren’t definitive or perfect for understanding how well a computer handles AI–other variables come into play in that sense–they do provide a good reference regarding the NPU’s speed, and they are helpful when making comparisons with competitors.
AI is part our present and our future, both in professional and domestic settings. Because of this, manufacturers are including another chip in today's computers called the NPU, which is in charge of AI functions, as well as handling operations in neural networks.
With the advent of this new type of processor, we also need a unit of measurement to refer to its capabilities and compare it with both competitors and previous generations. This is where the term TOPS comes in.
What Makes This so Important
As we mentioned earlier, one of the main uses of this metric is to discuss a computer’s power to run AI models. In other words, it’s used to indicate the speed at which AI applications run on a computer: The more TOPS, the faster the response.
In addition, TOPS are important in training AI models, especially large and complex ones. These processes require a vast number of calculations, and a higher performance means a faster completion; this, however, is beyond the abilities of home computers.
This is also a crucial measure in the development of new algorithms and architectures in neural networks. The computational power of the device depends on this: More TOPS mean better simulation and experimentation with complex models.
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