Generative AI: Transforming Inference At The Edge

The parallel nature of transformers makes them a good fit for resource-constrained edge devices.


The world is witnessing a revolutionary advancement in artificial intelligence with the emergence of generative AI. Generative AI generates text, images, or other media responding to prompts. We are in the early stages of this new technology; still, the depth and accuracy of its results are impressive, and its potential is mind-blowing. Generative AI uses transformers, a class of neural networks that learn context and meaning by tracking relationships in sequential data, such as the words in a sentence.

Most popular deep-learning architectures rely on extensive sequential processing. For example, recurrent neural networks (RNNs) are created by learning sets of weights to connect sequences of nodes. Convolutional neural networks (CNNs) iteratively perform an element-wise multiplication between an array of features called a kernel and the input of array numbers called a tensor, creating a feature map applied to the next layer. In contrast, transformers do not rely on sequential processing and instead use attention. Attention uses mathematical techniques to detect subtle ways elements in a series influence and depend on each other. This approach, which ultimately discerns global dependencies between input and output, has proven highly successful with large language model (LLM) applications like ChatGPT, Google Search, Dall-E, and Microsoft Copilot.

Transformers are far more potent than other model architectures. They are amenable to edge applications because the models can be highly compressed, are less data-hungry, and enable a high degree of parallel execution. They are now broadly applied in edge applications, for example, to reduce the bandwidth of 5G radio networks, to re-create digital avatars for video conferencing, and for image recognition. Transformer models are becoming indispensable to the future of edge AI inference and are reshaping the landscape of intelligent devices and applications.

Bringing inference to the edge

Traditionally, AI models were designed to run on powerful centralized servers or cloud infrastructures with high-speed internet connections. However, there are numerous advantages to moving AI inference to the edge where data is generated. This decentralized approach moves computation closer to the data source, thereby reducing latency, improving privacy, and strengthening data security while dramatically lowering bandwidth requirements.

Inference at the edge is challenging since edge devices are typically resource-constrained. They often lack sufficient computing and memory resources to run large and cumbersome conventional machine learning models efficiently. Furthermore, traditional models fail to capture long-range dependencies and context, making them less adept at understanding complex relationships in sequential data like language or time series.

Attention is all you need

In 2017, Vaswani et al. introduced transformers in the seminal paper “Attention Is All You Need.” The paper describes a new model architecture based solely on attention mechanisms and eliminates the need for recurrence and convolutions. Attention is a unique mechanism for processing sequential data that can effectively capture long-range dependencies. The paper presents results from two machine translation tasks demonstrating the models’ superior quality and ability to parallelize execution and significantly reduce training time.

A fit for resource-constrained devices

The parallel nature of transformers significantly increases their computational efficiency, making them a good fit for resource-constrained edge devices and real-time processing applications. The ability to run transformers on edge devices enables them to perform complex tasks autonomously without relying on a persistent internet connection or cloud infrastructure, enabling AI in edge applications such as autonomous vehicles, smart appliances, and industrial automation.

Another advantage of transformers is a smaller model footprint. Advances in model compression techniques, including knowledge distillation and pruning, allow developers to create more compact versions of their transformer models without sacrificing accuracy. These smaller models require less memory and storage and can be deployed on edge devices with limited hardware resources, empowering them to make intelligent decisions locally.

Learning on the job

Transformer models are capable of transfer learning and federated learning at the edge. Transfer learning leverages models pre-trained on vast datasets and fine-tunes them with smaller datasets specific to the edge application. It drastically reduces the need for large-scale data collection on edge devices while maintaining high performance. Similarly, federated learning allows multiple edge devices to train a global model collaboratively without sharing raw data, preserving data privacy and security.

Good with words

Transformer models excel at Natural Language Processing (NLP). Tasks like speech recognition, sentiment analysis, and language translation have significantly improved since the introduction of large-scale pre-trained language models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). By deploying such models at the edge, we enable real-time language understanding and interaction with devices, propelling the development of advanced chatbots, voice assistants, and personalized services.

Making it personal

Running sophisticated AI models on the device, users can enjoy tailored recommendations, adaptive interfaces, and personalized content without compromising their data privacy. Transformers open the door to a highly customized user experience and reduce dependency on cloud services for personalization tasks, creating a smoother and more private user experience.

The transformative capabilities of these models, including parallelism, computational efficiency, small memory footprint, and real-time natural language processing, open a world of possibilities for intelligent edge applications. By empowering edge devices to process complex data and make smart decisions locally, transformers promise a future where edge AI seamlessly integrates with our daily lives, revolutionizing industries and enriching user experiences in ways we could have only imagined before.

Expedera transformer support

Expedera’s packet-based Origin architecture supports transformers “out of the box.” Indeed, we have for some time – contact us for more information on how we can help with your transformer-based edge AI needs.

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