From edge to cloud: The critical role of hardware in AI applications

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The rise of generative artificial intelligence

In this new blog series, we explore artificial intelligence and automation in technology and the key role it plays in the Broadcom portfolio.

This Easter, I tasked Midjourney, the AI tool that generates art from text, to create a futuristic egg basket that showcased the concept of being digitally connected. What I saw blew my mind away. The artwork I received was not only visually stunning but also showed how AI is capable of bringing new ideas to life. I was experiencing first-hand, as a creator, the transformative nature of generative AI with Midjourney, chatGPT, and other tools.

Nowadays, Artificial Intelligence is steadily infiltrating everyday life. There is greater recognition of the potential of AI and a substantial amount of software programs and resources are now available. Its presence is becoming increasingly necessary in various sectors, such as healthcare, finance, and transportation.

The effects of this on the digital supply chain and technology hardware industry are enormous and cannot be ignored. The applications and software have to be able to take in data, process it, and return it quickly to the user. And this must be done on a large scale given how quickly the system is growing.

It is essential to continually develop new hardware in order to keep up with the digital revolution. Taking into consideration the ways Artificial Intelligence has impacted hardware and design, my experience as a content creator employing Midjourney for art projects or chatGPT for editorial assistance like this blog, broadens my perspective on the grand scheme of things.

So what does it take on the hardware side?

An increased demand for high-performance computing for cloud data centers

The necessity of managing complex algorithms and vast amounts of data has spurred the engineering of fresh processors, including GPUs (graphics processing units), FPGAs (field-programmable gate arrays), and customized AI silicon that are tailored specifically for dealing with AI workloads.

Memory and storage

Given the considerable output of information from AI operations, strong storage systems which can manage both arranged and unstructured data are essential. Utilizing high-power solid-state drives (SSDs) and non-volatile memory express (NVMe) allows for a faster rate at which information is both read and processed.

Wired connectivity as the binding thread

Moore’s Law does not accurately measure the speed at which the current data needs from AI can be processed. As a result, having efficient connections between computers working together simultaneously is key. Wired networks link the computing devices, GPUs, storage, and memory, and AI has shown that the network is a fundamental part of the computer and its connectivity is essential.

Wireless at the edge

It’s obvious that our digital activities are now wireless. So, it is essential that our wireless broadband networks can manage speedy data with low latency on a large-scale. Cellular technologies and Wi-Fi complement each other to satisfy this necessity. With the introduction of 5G, cellular networks can handle higher capacities and offer faster speeds. Together with the 6GHz band, Wi-Fi 7 technological advancements have decreased edge latency while preserving top speeds.


With the increasing use of AI, cybersecurity is becoming increasingly important to protect against cyber threats and attacks. This requires specialized hardware and software, such as intrusion detection systems and encryption technologies.


Before I close, let me also briefly preview some of the innovation we focus on.


Advanced manufacturing processes: These are vital for the production of AI hardware. The use of 7-nanometer and 5-nanometer manufacturing processes creates smaller and more powerful chips. Our chips carry higher data loads, and deliver the low latencies needed for AI.

Custom designs: We innovate data center storage and connectivity solutions that are optimized for specific AI workloads.

Power efficiency: Complex workloads require larger amounts of power, which can lead to increased energy costs among other things. This is an area of focus for both our wired and wireless chips. For example, on our Wi-Fi chips used in phones, we steadfastly work on radio optimizations and architectural modifications, each generation with an eye to disruptively lower power consumption.

These are just a few examples of our innovation focus. In a series of follow-up blogs, we are hoping to further delve into what Broadcom has to offer as AI takes off. We will keep you updated on how AI shapes the software and semiconductor hardware market and how our innovations are geared to keep pace with the demands of AI applications.

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