Enot News

Are you sure you are getting the most out of your neural networks? You might be missing out!

Most AI developers are implementing some type of optimizers for their neural networks. Some devs may use PyTorch’s inbuilt functionality, others spend dozens of hours on integrating with TensorRT, while in a few cases developers try to find the most optimal architecture manually with trial and error. In many cases, a method that is most commonly known and quoted is pruning. But there is so much more to neural network optimization.

But why optimize your neural nets in the first place? Optimization of neural networks essentially means that you consume less computational resources to achieve the same results. Depending on your use case, there are several benefits to be gained after optimization:

  • Speed up the neural networks on your existing hardware 2-5 times;
  • Introduce more features/neural nets on your existing hardware;
  • Reduce target hardware costs by up to 80%;
  • Reduce cloud computing costs by around 70%;
  • Reduce power consumption on your hardware;
  • Reduce development time and costs by up to 90%.

While the benefits are quite desirable, it is actually quite challenging and time-consuming to optimize neural networks to reach their fullest potential. Proper optimization involves changing the very architecture of your neural nets to find the best possible combination of a dozen input parameters. In other terms, good luck with brute-forcing through thousands of potential variations of those parameters to find the most optimal one for your specific use case.

To solve this issue, we founded enot.ai, where throughout the last 2 years we have built a state-of-the-art neural network architecture search engine that allows you to find the best possible architecture that fits your needs & requirements.

At a fraction of time, our tools can help you achieve the highest compression/acceleration rates with no or close to zero accuracy degradation. You can fully automate the search for your optimal neural network architecture, taking into account latency, accuracy, RAM and model size constraints for different hardware and software platforms, as well as a dozen additional parameters.

Our tools can be quickly and easily integrated within various neural network training pipelines and can be used by both junior and senior AI developers. Best of all, it takes a very small amount of time to set it up, yet your optimized neural nets will bring you performance advantages and cost savings for a long time.

We’d love to talk to you about how we can help you improve your neural network pipeline. Message us here on LinkedIn or leave your contact info on our website – www.enot.ai.

The first two months are on us – completely for free and no strings attached!