Storage Requirements Needed for Deep Learning: Avoid the Curse of Dimensionality
With the parameters deep neural networks must process, metadata has become viewed as the curse of dimensionality: a phenomenon “that arises when analyzing and organizing data in high-dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience.” An expression neologized by Richard E. Bellman when analyzing dilemmas in dynamic programming.
First of all, one should know that “artificial intelligence” is merely an umbrella term referring to one of many major fields belonging to data science. Scientific methods, processes, algorithms, and systems are the things that power machine learning, deep learning, and neural networks so that AI-powered machines can extract information and insights from structured and unstructured data sets.
As it is related to data mining and big data, “data science is a “concept to unify statistics, data analysis, machine learning, and their related methods” in order to “understand and analyze actual phenomena” with data.” [Hayashi, Chikio (1 January 1998). “What is Data Science? Fundamental Concepts and a Heuristic Example“]
Michael Schmidt wrote an article for TechCrunch in which he stated: “Apple’s Siri, Google’s self-driving cars and Facebook’s image recognition software are standard examples of AI. But it’s much broader than that. AI also powers product pricing on Amazon, movie recommendations on Netflix, predictive maintenance for machinery and fraud detection for your credit card. While these applications are all powered very differently and achieve different goals, they all roll up into the umbrella term of artificial intelligence.”
It Takes More Than Computing Power to Power AI
Evolutionary computation and distributed computing infrastructures aren’t all that powers machine learning, deep learning, and neural networks; powerful, robust, scalable storage platforms such as the X-AI Accelerated are optimized for ingest, training, data transformations, replication, metadata, and small data transfers. But why aren’t legacy storage solutions and graphics processing units (GPU) cut out for the job? Why isn’t it enough to have lots of storage and lots of processing power?
As the old saying goes: “It’s not only about what is done but how it’s done.”
When it comes to AI-powered systems, data storage platforms are required to do much more than store information. Imagine if the human brain only processed and stored the information it receives? Studies have long since proven that the human brain has roughly three types of memory: sensory, working, and long-term. Working memory is thought to store data for about 20 seconds or so, maintained by an electrical signal looping through a particular series of neurons for a short period of time, according to Teachnology.
Since the whole purpose behind AI is to create machines that think like humans, AI-powered machines must also process and store data the same as humans. This means machine learning needs storage that can deliver fast read-write access while also being capable of storing massive amounts of data without costing a ridiculous amount of money.
Much like the human brain, AI-powered machines use neural networks to achieve bottom-up and top-down processing. And even though modern SSDs offer 2.5 Gbps in most cases, “there still lies a significant bottleneck in the fact that data must still be moved into and out of the RAM,” as pointed out by TDWI.
” […] slow speed is usually attributed to the need to move the weight data back and forth between the memory and processor, which, if you recall, is the slowest step in traditional data processing,” the author continues.
Here Are the Storage Requirements for Deep Learning
Deep learning workloads are a special kind of beast: all DL data is considered hot data, which raises the dilemma of not being able to employ any sort of tiered storage management solution. This is because normal SSDs usually used for hot data under conventional conditions simply won’t move the data required for millions, billions, or even trillions of metadata transfers for an ML training model to classify an unknown something out of only a limited amount of examples.
Below are a few examples of a few storage requirements needed to avoid the dreaded curse of dimensionality.
Cost Efficiency
Enormous AI data sets become an even bigger burden if they don’t fall within the budget set aside for storage. Anyone who has been in charge of managing enterprise data for any amount of time knows well that highly-scalable systems have always been more high-priced on a capacity versus cost basis. The ultimate deep learning storage system must be both affordable and scalable to make sense.
Parallel Architecture
In order to avoid those dreaded choke points that stunt a deep learning machine’s ability to learn, it’s essential for data sets t to have parallel-access architecture.
Data Locality
While it might be possible that many organizations may opt to keep some of their data on the cloud, most of it should remain on-site in a data center. There are at least three reasons for this: regulatory compliance, cost efficiency, and performance. For this reason, on-site storage must rival the cost of keeping it on the cloud.
Hybrid Architecture
As touched on above, different types of data have unique performance requirements. Thus, storage solutions should offer the perfect mixture of storage technologies instead of an asymmetrical strategy that will eventually fail. It’s all about simultaneously meeting ML storage performance and scalability.
Software-Defined Storage
Not all huge data sets are the same—especially in terms of DL and ML. While some of them can get by with the simplicity of pre-configured machines, others need hyper-scale data centers featuring purpose-built servers architectures that are previously set in place. This is what makes software-defined storage solutions the best option.
Our X-AI Accelerated is an any–scale DL and ML solution that offers unmatched versatility for any organization’s needs. X-AI Accelerated was engineered from the ground up and optimized for “ingest, training, data transformations, replication, metadata, and small data transfers.” Not only that but RAID Inc. offers all the aforementioned requirements such as all-flash NVMe X2-AI/X4-AI or the X5-AI, which are hybrid flash and hard drive storage platforms.
Both the NVMe X2-AI/X4-AI and the X5-AI support parallel access to flash and deeply expandable HDD storage as well. Furthermore, the X-AI Accelerated storage platform permits one to scale out from only a few TBs to tens of PBs. Contact us today to learn more about our X-AI Accelerated storage solutions for DL and ML models.
