Designs for Overcoming Deep Learning Infrastructure Challenges Overcoming Deep Learning Infrastructure Challenges


With use cases like computer vision, natural language processing, predictive modeling and more, deep learning (DL) provides the kinds of far-reaching applications that are changing the way technology can have an impact on human existence. The possibilities are limitless, and we’ve only just scratched the surface of its potential.

But designing an infrastructure for DL ​​creates a unique set of challenges. Even the training and inference stages of DL have distinct requirements. You typically want to run a proof of concept (POC) for the training phase of the project and another for the inference part, as the requirements for each are quite different.

Deep Learning Infrastructure Challenges

You should be aware of three important hurdles when designing a deep learning infrastructure: scalability, customization for each workload, and workload performance optimization.


The hardware steps required to set up a DL technology cluster each present unique challenges. Moving from POC to production often ends in failure, due to the added scale, complexity, user adoption, and other issues. You should design scalability into hardware from the start.

Custom Workloads

Specific workloads require specific customizations. You can run ML on a non-GPU accelerated cluster, but DL generally requires GPU-based systems. And training requires the ability to support the ingestion, output, and processing of large datasets.

Optimize workload performance

One of the most crucial factors in building your hardware is optimizing performance for your workload. Your cluster should be modular in design, allowing for customization to meet your primary concerns, such as networking speed, processing power, etc. This version can evolve with you and your workloads and adapt to the emergence of new technologies or new needs.

Infrastructure requirements for DL ​​processes

Training an artificial neural network requires you to organize huge amounts of data into a designated structure and then feed that massive training dataset into a DL framework. Once the DL framework is trained, it can take advantage of this training when exposed to new data and make inferences about the new data. But each of these processes has different infrastructure requirements for optimal performance.


Training is the process of learning a new capability from existing data based on exposure to related data, usually in very large amounts. These factors should be considered in your training infrastructure:

  • Get as much raw compute power and as many nodes as you can allocate. You should use multi-core CPUs and GPUs because training your AI model accurately is the most critical issue you will face. It can take a long time to get there, but the more nodes and more mathematical precision you can fit into your cluster, the faster and more accurate your training will be.
  • Training often requires the gradual addition of new datasets that remain clean and well-structured. This means that these resources cannot be shared with others in the data center. You should focus on optimizing this workload for better performance and more accurate training. Don’t try to build a general-purpose compute cluster assuming it can take on other tasks in its spare time.
  • Huge training datasets require massive networking and storage capabilities to retain and transfer the data, especially if your data is image-based or heterogeneous. Plan for adequate networking and storage capacity, not just for powerful computing.
  • The biggest challenge in designing hardware for training neural networks is scaling. Doubling the amount of training data does not mean doubling the number of resources used to process it. This means exponential expansion.


Inference is the application of what has been learned to new data (usually through an application or service) and making informed decisions about the data and its attributes. Once your executive is trained, they can then make educated guesses about new data based on the training they received. These factors should be considered in your inference infrastructure:

  • Inference clusters should be optimized for performance by using simpler hardware with less power than the training cluster but with the lowest possible latency.
  • Throughput is critical to inference. The process requires high I/O bandwidth and enough memory to hold both the required training model(s) and input data without having to recall cluster storage components.
  • Data center resource requirements for inference are typically not as large for a single instance compared to training requirements. This is because the amount of data or the number of users an inference platform can support is limited by the performance of the platform and the requirements of the application. Think of speech recognition software, which can only work when there is a clear input stream. Multiple input streams render the application unusable. It’s the same with inference input streams.

Inference on the edge

There are several special considerations for edge inference:

  • Computers at the edge are significantly less powerful than the massive computing power available in data centers and the cloud. But it still works because inference requires much less processing power than training clusters.
  • If you have hundreds or thousands of instances of the neural network model to support, remember that each of these multiple incoming data sources requires sufficient resources to process the data.
  • Normally you want your storage and memory to be as close to the CPU as possible, to reduce latency. But when you have peripheral devices, memory is sometimes far from the processing and storage components of the system. This means you need a device that supports edge GPU or FPGA compute and storage, and/or access to a high-performance, low-latency network.
  • You can also use a hybrid model, where the edge device collects the data but sends it to the cloud, where the inference model is applied to the new data. If the inherent latency of moving data to the cloud is acceptable (not the case in some real-time applications, such as self-driving cars), this might work for you.

Achieving DL Technology Goals

Your goals for your DL technology are to drive AI applications that maximize automation and allow you a much higher level of efficiency in your organization. Learn even more about how to build the infrastructure that will achieve these goals with this white paper silicon mechanics.


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