Artificial intelligence (AI) processes, like machine learning (ML) and deep learning (DL), have revolutionized the way organizations operate, streamlining operations while supporting more strategic decision-making. For example, AI systems can refine and personalize customer experiences and services, help autonomous robots work more accurately and safely, and support healthcare decisions by analyzing patient data.

However, training AI models for these use cases requires significant bandwidth and computing resources during training and deployment. The cost of resources can be a major barrier for enterprises aiming to develop AI. At the same time, leading innovators must also optimize AI processes for a greater return on investment (ROI).

To mitigate the expense and scarcity of computing hardware, companies training AI models can use several techniques to streamline and scale AI processes more effectively. This helps reduce costs and makes AI innovation accessible to more organizations. However, you must approach optimization techniques strategically to ensure high-quality outputs. 

6 techniques to accelerate and optimize AI systems 

There are several common methods enterprises can use to streamline AI development and deployment. Generally, these techniques aim to reduce a model’s size, accelerate the training process, or conserve computing resources. Using one or more of these approaches can significantly expedite development timelines and lower enterprise costs. 

1. Transfer learning 

Transfer learning uses a pre-trained model as a starting point to train another model in a similar domain. Once a model is trained on one task, repurposing it for training on a separate but related task expedites progress and eliminates the need to train other models from the ground up. 

Transfer learning techniques are most effective when the initial model can be generalized. This means its knowledge is relevant for both tasks, not just the first. For example, imagine you have a model trained to detect malignant tumors in digital breast tomosynthesis (DBT) images. You could use transfer learning to adapt this model for a similar task, like identifying a different type of tumor in radiological imagery.

This approach can streamline deep learning processes, which typically need an abundance of data and computing power. Because transfer learning requires fewer data and computing resources, it reduces development costs and timelines. It is ideal for developing multiple models for related tasks and can support better neural network performance in these applications. It’s also useful if your organization doesn’t have enough high-quality data for a specific problem to develop a model from scratch. 

2. Model pruning 

AI models like OpenAI’s ChatGPT are powerful at responding to a wide range of prompts because they are trained with millions or even billions of parameters. Parameters define the semantic relationships a model forms between words and phrases in training datasets. A model automatically learns these connections during training.

The more parameters a model generates, the more computational and memory requirements it will need for training and inference. However, many of these parameters are irrelevant or redundant for a specific task, similar to how the human brain stores unused information. Even high-performing neural networks will contain needless parameters.

Model pruning is the process of eliminating unnecessary parameters for your desired task—those playing no significant role in improving performance. Measured in weights, parameters with higher-value weights have a greater influence over model outputs. With pruning, lower-value weights are adjusted to zero so they no longer impact model performance.

This approach can enable a model to achieve similar performance to its non-pruned equivalent while operating more efficiently. As a result, models are generally faster and less expensive to develop and use. Because they require less computing power, pruned models can be deployed on smaller devices or computers, such as mobile phones, without graphics processing units (GPUs).

Pruning can also address performance issues like overfitting, which produces unreliable outputs when training data contains too much noise.

3. Hyperparameter tuning 

Hyperparameter tuning involves experimenting with different variables that influence how a model learns. Hyperparameters include factors like how many layers a neural network has, how many neurons occur in each layer, or the number of epochs (how many times a training dataset passes through the model’s algorithm). Developers set these hyperparameters manually, dictating a model’s learning behavior and, ultimately, what parameters it creates.

Each hyperparameter combination results in different levels of model performance and efficiency. Because there are no universally effective hyperparameter values, AI practitioners must experiment with different configurations to find which is best for a given model. After each iteration, developers use statistical analysis to evaluate results, tweaking the next set of hyperparameters to optimize performance further.

Hyperparameter tuning is crucial for ensuring high-quality and accurate outputs. With the right combination of hyperparameter values, enterprises require less time, computing, and bandwidth resources to train and run more efficient AI models. 

4. Neural architecture search 

Hyperparameters aren’t the only variables contributing to neural network efficiency. Developers must also configure other aspects of neural network architecture, including training data, inference hardware, or types of neural network layers, to optimize performance. Neural architecture search (NAS) automates neural network design, a process traditionally performed by humans.

According to researchers, NAS can facilitate faster development and improve network architecture compared to human-created designs. It is often used to automate hyperparameter tuning, using an algorithm to generate hyperparameter values rather than relying on developers to run experiments manually.

Developers implement NAS using three main methods: 

• Search space. They determine which basic structures or building blocks the NAS algorithm can operate within. Creating a large search space with many options promotes more complex and sophisticated neural architectures but has higher computational costs. 
• Search strategy. They dictate how the NAS algorithm experiments with neural network architectures to find the optimal design. While algorithms can be developed to choose and test various architectures at random, approaches like reinforcement learning are more efficient and effective at finding high-performance configurations. 
• Evaluation strategy. They equip the NAS algorithm with tools to assess and compare performance between different neural network architectures. This allows the algorithm to select the best option for a desired task. 

5. AI quantization

AI aside, quantization is the practice of reducing the number of bits, or units of data, used to express information. For example, a video with a high bitrate appears sharper and of higher quality when compared to a quantized video with a lower bitrate.

The same principle applies to AI and the number of bits used to represent parameter weights. Typically, parameter weights operate at 32 bits for full precision. AI quantization reduces these weights to 24, 16, 8, or 4 bits, lowering the model’s precision as it makes computations. While neural networks can be quantized after training, quantizing during training helps maintain greater output accuracy.

Quantization can lead to a 4x reduction in model size to create a faster system with lower energy, computational, and memory bandwidth requirements. Organizations may use quantization when an AI product’s speed-to-market is an important competitive differentiator. Like pruning, AI quantization allows for deployment on smaller devices, yet research shows the approach can outperform pruning techniques in both efficiency and model accuracy. 

6. Deployment runtime modification 

While developers can use techniques like AI quantization, model pruning, and hyperparameter tuning to build more efficient models, they can also optimize a model’s operating environment to improve performance. For instance, enterprises can upgrade computing hardware from a central processing unit (CPU) to a GPU, or a GPU to a more current, powerful version, to accelerate computation.

Developers can also optimize AI frameworks and libraries, foundational tools for supporting the AI development lifecycle. Note that combining incompatible software and hardware can be inefficient, so select libraries and frameworks that best fit your underlying hardware.

If your organization runs AI models with multiple types of frameworks and environments, such as the cloud, edge devices, GPUs, and CPUs, improving model efficiency across platforms can be a complex challenge. This is because each environment has unique requirements and capabilities that should be optimized on a case-by-case basis. In this situation, consider solutions like the Open Neural Network Exchange Runtime (ONNX) to simplify and streamline deployment. 

Considering the tradeoffs of AI model optimization 

These optimization techniques deliver significant cost, energy, and time savings. However, they can also jeopardize model performance when misused. For example: 

• Transfer learning using pre-trained models without sufficient data quality or domain expertise can cause issues like overfitting and compromise output quality. 
• Over-pruning a model can leave it without enough parameters, decreasing output accuracy and reliability. 
• Model performance varies with different quantization approaches. Researchers have discovered larger networks with lower precision tend to outperform smaller networks with higher precision in accuracy and efficiency. 

It’s important to weigh the pros and cons of each optimization technique and determine a strategy that best balances output quality with overall efficiency. This balance will look different for every use case. Evaluate your model’s performance continuously to ensure efficiency gains don’t impact long-term outcomes, and adjust your optimization methodologies as needed. 

Balancing AI model accuracy with efficiency to ensure long-term success 

Transfer learning, model pruning, and NAS effectively accelerate AI model development and reduce computational, energy, and bandwidth requirements. These approaches can help your organization lower development costs for a greater ROI while improving time-to-market for competitive AI solutions. 

Greater AI efficiency is also key to more responsible development practices, helping enterprises reach sustainability targets without sacrificing innovation. If resource constraints are a barrier to your organization’s transformation, AI optimization can empower you to participate in development projects that would otherwise be cost-prohibitive.

Regardless of which techniques you use, it’s crucial to approach AI model optimization as an ongoing effort. That way you can ensure your AI systems remain as efficient as possible without compromising model accuracy and quality. 

Learn more about Outshift by Cisco’s approach to responsible AI.