Adding Efficiency to a Neural Network

Adding Efficiency to a Neural Network IT Network - Negosentro

Negosentro | Adding Efficiency to a Neural Network | Computing power for the typical consumer has crested. The demand for more computing power is lower than decades before as we can see by the purchasing behavior of household consumers and even mid-level tech businesses. It seems the top software companies are using distributed computing in an on-demand setting for large workloads, but the employees of these tech firms are just using laptops to send in their work from home or the office. Even a laptop a few years old is fast enough to suffice the average user. The only remaining household consumer of beefy computing is the typical computer gamer, but they are on a decline in recent years due to the overwhelming demand for graphics cards brought on by the onset of cryptocurrency mining. Artificial intelligence boasts an impressive turn for the future, yet it also requires heavy application-specific computing power – much the same as cryptocurrency mining. For AI to be of any use, it must be fast. Self-driving cars don’t have the luxury of waiting for data to get processed in batches off-site. Where the rubber meets the road right now in terms of trading electricity burned for computing power in the most efficient way possible, neural networks are the standard for machine learning. Like all software before it, the name of the game is efficiency. When it comes to neural network engineering, here are some ways currently being used to improve efficiency in learning designs. 

Increase Using Brute Force 

The next bump-up in magnitude for computing power isn’t scheduled to happen until quantum computers become commonplace. While they’ve hardly been invented yet, in theory, they do have the power to solve many of mankind’s problems. This is too far away to be included yet, so the current round-up for pure power in computing comes from designing hardware around the software. This is called ASIC computing, or application-specific integrated circuits. Graphics processing units made by nVidia and AMD are the closest thing to a pure ASIC computer for any given AI task. It’s not uncommon to see large companies utilize thousands of GPUs at a time in a distributed pattern to teach a neural network. 

Increase Using Better Programming

From the software perspective, there’s always room for improvement by rewriting the code. Software engineers are constantly updating code to newer revisions because they know the next scheduled version of any massively used software has to be quicker and use less power to run the same thing. Self-driving cars are a good example of a multi-layered neural network that is always growing. Google and Microsoft use neural networks to help identify images and videos when searching the internet with their software. Teaching a computer to think more like a human is not a new idea at all, but the way that computers today are doing machine learning is actually pretty new. 

Increase Using Larger Samples

When feeding data to an AI learning program, the neural network batch size is like the size of the samples being fed into the computer. A learning project might require showing the computer 100,000 pictures of something before it can properly identify what that thing is versus other items in its knowledge base. If the particular network runs most efficiently at learning, say 100 pictures per analysis cycle, then those 100 pictures are the batch size. Knowing the best batch size also lets us know many other factors about the strengths and weaknesses of a particular deep learning network.

Increase by Going Greener

While quantum computing is still being dreamed up and ASIC computing is on the rise, the very real tradeoff for humans pushing the envelope for AI is energy consumption. Taking a look at cryptocurrency mining and heavily distributed computer compiling tasks, burning electricity is the unavoidable consequence of going faster and teaching artificial intelligence more each day. By improving our processing power per watt consumed and by dreaming of ways to do more with less, we can improve AI learning efficiency by consuming less energy at the end of the day. It’s important that we continue developing more efficient computer chips and even more efficient ways to harness and distribute electricity.

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