Successful ML Project: Better Hardware vs Optimized Algorithm

Successful ML Project: Better Hardware vs Optimized Algorithm

The role of algorithm performance, computational power, or hardware capabilities in implementing effective machine learning (ML) models is not immediately obvious despite its importance to any AI project.

To be successful in any endeavor, regardless of domain, you will most likely require both. This recommendation is reasonable when comparing the significance of a strong algorithm architecture and fast hardware to experiment on.

But, the devil is in the details. And a lot depends on whether you're developing an ML model to forecast house prices for the next five years or training a model to detect cancer in medical photos. This is why.

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Algorithm Improvement Matters: A Study By MIT

A group of scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) submitted a research report in 2020 that demonstrated the necessity of improved algorithms in increasing computing against hardware performance.

The researchers examined 113 algorithm families, which are groups of algorithms that enable solutions to the same problems, and tracked their evolution since 1940. As a result, researchers discovered that the complexity of an algorithm matters more than the technology utilized in the process when it comes to the success of a computer activity.

These findings sparked debate in the IT community, prompting some software and machine learning engineers to doubt the impact of Moore's Law, which states that processing power doubles every two years, on the efficiency of algorithm-based jobs.

Nevertheless, no matter how flawless your algorithm architecture is, you won't get anywhere unless you have very efficient hardware to test it on.

The Role Of Hardware In Performance

The intricacy of the algorithm architecture influences the hardware selection for a certain project. The more complicated the architecture, the more parameters it has, the more powerful the hardware required to execute the computational tasks that it was created to solve.

It can take several days to train a neural network with up to 3 million parameters. If a project necessitates a more advanced design with more parameters, computation time will be increased. High-performance hardware can shorten the programming cycle. It accelerates iterations, allowing the team to experiment with the neural network faster and achieve the desired outcome in less time.

Graphical Processing Units (GPUs), Tensor Processing Units (TPUs), and Central Processor Units (CPUs) are industry standards for hardware in machine learning applications (CPUs).

  • The following are the results of a survey conducted by the National Institute of Standards and Technology. They excel at parallel processing and can do a large number of arithmetic operations at the same time, speeding up the completion of a given task, including in ML. (Among of my favorites are the NVIDIA A100, NVIDIA Tesla T4, NVIDIA Tesla P4, NVIDIA Tesla V100, and NVIDIA Tesla K80.)

  • TPUs are specialized integrated circuits that help AI calculations and algorithm applications run faster. Google, for example, provides cloud TPUs that enable neural network training in the cloud without the need to install any additional hardware or software.

  • CPUs manage all computer functions, including basic arithmetic and program input/output. They may not be the greatest solution for algorithm-related jobs because they were built to do a wide range of computational tasks, but they are still employed in the market.

The bottom line is that the faster the team can provide the required outcome, the better the chosen hardware will work. But, in some fields, technical competence and accuracy are more important than neural network delivery speed.

Performance And Project Domain

How do you calculate the efficiency of a trained machine learning model or neural network? Engineers typically rely on two essential metrics: model accuracy and the timeframe in which the required precision may be reached.

While the former is influenced by a model's architecture and how well hyperparameters that define the learning process are configured, the later is mostly determined by hardware performance.

When dealing with a complex activity that involves high risks, such as training a neural network to detect early indicators of cancer in X-Rays or detect possibly malignant lesions in skin, the algorithm development and technical experience of skilled engineers is critical.

The Businessware Technologies ML engineering team has years of experience in training extremely effective ML models. While approaching an ML project, we methodically create a pipeline and continue to improve the model until we achieve the best potential result. Using this method, we were able to develop an efficient hand-tracking model for a virtual ring try-on that precisely measures the size of a finger, virtually fits a ring on it in the correct location, and renders realistically.

If you require expert assistance in developing ML models, please leave your contact information and we will contact you to discuss your project.