Revolutionary technologies, like machine learning, Artificial intelligence and many others, have become popular across the world with their untapped potential of performing data analysis. Machine Learning, however, allows business sectors to discover meaningful insights, enhance productivity, and operate efficiently.
Machine Learning is derived from Artificial Intelligence that enables systems to learn new things by taking feedback from users and observing their data without being programmed explicitly. Developing machine learning capability requires a lot of mathematical and probabilistic computation models. Many mathematical problems are complex for humans, but computational machines can solve similar issues considerably easily and quickly.
Normal consumer hardware is not as capable as deep neural networks to perform extensive computations quickly. Organizations, however, can achieve required extensive calculations with ease if they choose appropriate hardware specific to machine learning projects.
But before we start defining the best hardware for the Machine Learning project, let’s clear the flow of the implementation of the machine learning project.
Steps for implementing machine learning models:
- Preprocessing input data
- Training the deep learning model
- Storing Trained deep learning model
- Model deployment
In all these steps, training the machine learning model is the most intensive task computationally. If we use training the ML model without using appropriate hardware, the task becomes more frustrating. However, ML model training is an integral and intensive part of the neural networks; therefore, it requires various matrix multiplications type mathematical computations.
Differentiating the Possible Hardware Choices for Machine Learning Model:
Machine learning algorithms vary according to the computation intensity. Deep neural networks or support vector machines are the most popular algorithms because they are computationally intensive. Hence, to train such models, numerous computing resources are required.
Choosing appropriate Machine Learning hardware is a complex process and requires a lot of human expertise and effort. So, let’s evaluate three necessary hardware options for a machine learning project: Processing Units, Memory, and Storage.
Central Processing Unit (CPU):
Nowadays, GPUs are widely used for machine learning processing, whereas CPUs are primary units used for information preprocessing. CPUs are specifically designed for serial operations; therefore, they support a large amount of cache memory with fewer cores to quickly complete the processing of complicated instructions.
CPUs are only limited to executing a few complex operations in sequence quickly, in which machine learning involves parallel processing of a large quantity of simple linear algebra computations. However, a machine learning project does not require complex algorithms and compute resources, making the CPU the best choice over GPU.
Many developers say that the CPU is understaffed rather than overpowered; thus, it cannot compete with GPUs.
Graphical Processing Unit (GPU):
GPUs are capable of extensive parallel computing and provide higher throughput, as well as they can perform maximum computation in real-time since their architecture contains thousands of smaller cores than conventional CPUs.
The main motive behind creating GPU was to utilize it for graphical processing workloads, but afterwards, it turned out to be the best fit for general-purpose computing in several fields, such as product design, science, etc. Machine learning data is mostly stored in matrix forms, as matrix multiplication is the most common procedure in mathematics. GPU can speed such computations up to 7 times faster compared to CPUs. In order to achieve such a level of speed, at first, it is important to consider machine learning model size fits in the VRAM memory of GPU.
With the growth of machine learning capabilities, applications also need improved memory configurations to manage increasing workloads while maintaining efficiency.
For machine learning applications, three primary types of memory considerations are there:
On-chip is the fastest memory type and is on the top of the memory hierarchy. It is fixed near to the compute cores of the CPU and GPU, which not only makes it faster but also limits the capacity. In most cases, it serves as a local cache memory for microprocessors.
High bandwidth memory:
High bandwidth memory is a 3D stacked memory type situated on the DRAM architecture. It provides high bandwidth and capacity, as well as enhances users’ performance. HBM is not efficient as On-chip memory but is an expert in maintaining low data rates and ensuring effective operation. For instance, HBME2 – the latest version of HBM is a viable memory option for various machine learning applications.
This memory option is widely used for gaming applications, but due to its efficiency, many experts advise using it for machine learning programs. The latest GDDR6 memory can support around 16GBPS of high data rates.
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Storage is not exactly a source of concern for machine learning projects but can be a point of concern, especially when it comes to budget. Also, more than 3TB capacity of HDD or SDD would be sufficient for any machine learning application. If your budget is big enough to buy a 599GB NVMs M2 SSD, it could be a good choice.
Since most of the application data would be residing in RAM or GPU VRAM, storage concerns should not be there for the testing purpose, but still having SSD over HDD can be better and worth the price.
Accelerating ML performance by Choosing the Appropriate Hardware:
Quality ML hardware is needed for any processing of the pipeline, whether you are utilizing existing architecture or developing a new one. The appropriate hardware selection brings assumed lower latency inference, lower power consumption, higher throughput training, and above all, lower power costs. Though there are multiple choices for machine learning hardware products present in the market for different computation needs, many people find difficulties in choosing the best one to upgrade when assuming its after impact on performance.
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