Artificial Intelligence Hardware is a Hot Tech Topic in 2022

In this article, we will cover some of the most trendy Artificial Intelligence hardware of 2022

The application of artificial intelligence in many companies has expanded as a result of the epidemic. Since the worldwide COVID-19 outbreak, artificial intelligence's potential value has only grown. At least 50 percent of the organizations have deployed Artificial Intelligence functions, as per the McKinsey State of Artificial Intelligence study released in November 2020.

As firms continue more automation of day-to-day operations & better analyses of COVID-affected datasets, Artificial Intelligence will become more important. Businesses are more digitally linked than ever before since lockdown and work-from-home rules were introduced.

Artificial intelligence will continue to evolve until 2022 when it will be the most transformative technology humanity has ever devised.

However, with the rise of artificial intelligence, the scenario for semiconductor businesses may be different. Many AI applications, such as virtual assistants who run our homes or facial recognition algorithms that monitor criminals, have already earned a large following. These and other developing AI applications have one thing in common: they rely on hardware as a key facilitator of innovation, particularly for logic and memory tasks. What impact will this have on semiconductor sales and profits?

Artificial Intelligence has the potential to allow semiconductor businesses to capture 40 to 50% of the overall value from the technological stack, which would be the finest chance in decades. The fastest-growing sector will be storage, while semiconductor companies will make the most money in computing, memory, and networking.

Existing framework

Compute:        Accelerators for parallel processing, such as GPUs and FPGAs

Memory :         High-bandwidth memory or On-chip memory (SRAM3)

Storage:          Expansion ability for potential growth in data.

Networking:     Data centers

Potential new framework:

Compute:        Workload-specific AI accelerators

Memory:          Emerging non-volatile memory (NVM) (as memory device)

Storage           AI-optimized storage systems and Emerging NVM (as a storage device)

Networking:     Programmable switches or High-speed interconnect

Computing power:

At cloud computing data centers, the majority of compute growth will come from increased demand for Artificial Intelligence applications. GPUs are currently employed in practically all training applications at these sites. They will lose business to ASICs in the near future, with the compute market almost evenly split between these two technologies. GPUs will likely get increasingly tailored to fit the demands of DL when ASICs join the market. FPGAs and GPUs will play a minor part in Artificial Intelligence training in the future.

Memory/Storage:

Because computational layers inside deep neural networks must send input data to hundreds of cores as rapidly as feasible, AI applications have significant memory-bandwidth requirements. During both inference and training, memory—typically dynamic random-access memory (DRAM)—is necessary to store incoming data, weight model parameters, as well as perform other activities. The memory industry will see significant growth in value from $6.4 billion in 2017 reaching $12.0 billion in 2025, thanks to AI. Using DRAM or other external memory sources to store and access data on a DL compute processor can take 100 times longer than using memory on the same device. The tensor-processing unit (TPU), an ASIC specialized in Artificial Intelligence, was built by Google with enough memory to hold a complete model.

Networking:

During training, Artificial Intelligence applications require a large number of servers, a requirement that grows over time. Developers, for example, just with only one server to create an initial Artificial Intelligence model and less than 100 to refine its structure. The natural next step, though, is to train with real data, which may take several hundred hours. To identify impediments with 97 percent accuracy, autonomous-driving models require around 140 servers.

Way forward:

When bringing a new product to market, semiconductor businesses should think in terms of partnerships, since cooperating with existing players in specialised industries may provide them a competitive advantage. They should also figure out which organisational structure is ideal for their company. They may wish to form groups that specialise in specific functions, such as R&D, for all industries in some situations. Alternatively, they may form groups to focus on certain micro verticals, allowing them to gain specialised knowledge.

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