Impact of Global Semiconductor Chip Shortage

 

Almost every business on the planet is being impacted by the global chip shortage. Consumers are already seeing it directly as planned gadgets and appliances are delayed or in short supply — but they may become much more frustrated when tech's promises of an AI-powered future fall short due to a lack of hardware to operate it on. However, hardware isn't the only method to make these powerful machine learning technologies work.

Over the last few years, demand for chips of all sizes and powers has steadily increased, and supply has mainly been able to keep up until the current production problem. To give you a sense of the scope of this somewhat complicated tech sector problem, cutting-edge smartphones and servers aren't the only goods affected by the scarcity. A wide range of consumer products wearables - smart watches, home automation technology, and automobiles, to mention a few — have lately been updated to "smart" status and will be impacted as a result.

The Current Chip Shortage Solutions

The tech industry has already begun to take significant steps to address the shortfall. The obvious thing to take is to invest in existing and new chip production facilities, which most businesses are already doing. Covid-19, on the other hand, has disrupted the supply networks that would keep these plants functioning at normal rates, let alone the expanded ones required to meet demand.

In reaction to market instability and political constraints, China's reliable suppliers have stockpiled and limited their exports, and efforts to make the United States and others more self-sufficient in electronics production are nowhere near fulfilment. To put it another way, while investment is necessary to keep the global chip market afloat, it is insufficient to narrow the gap in the short term.

A more promising strategy is to accommodate older chip technology, both in terms of production and engineering. When new model inventory runs out, you might consider turning to used automobiles. "Used" semiconductor equipment here refers to manufacturing capacity from past chip generations that is no longer cutting-edge but is certainly better than nothing.

Because of the large demand for used equipment during the pandemic, device manufacturers are working on new devices that utilize older chips. This has already helped to mitigate the effects of the scarcity, but it's a desperate effort for an industry that, like a shark, must always go forward or perish.

Meanwhile, billions of people use tens of billions of gadgets every day, all of whom may benefit from a more immediate answer: a software solution to a hardware scarcity.

Software Solutions: Smart Compression and Compilation

Unlike hardware, software can be deployed globally at the rate required to maintain the industry's promises of AI-powered cameras, speech and face recognition, augmented reality, and other technologies on track. Until date, the industry has been unable to deploy software as a solution to the chip scarcity and to develop AI models on edge computing devices due to inefficiency.

When it comes to machine learning, efficient compression and compilation are about much more than reducing download sizes. It's critical to analyze what aspects of a working model are crucial to its outcomes in order to lessen the size and power needs of that model. As a result, smart compression entails "pruning" the model by deleting layers, filters, or channels without compromising its accuracy. It also entails "quantization," or reducing precision to save calculation cycles.

Compilation converts the compressed model's high-level operations to the low-level operations supported by a chip's architecture.

The problem is that there is no one-size-fits-all solution for completing these critical tasks. A machine learning model’s complexity must be compressed and assembled with the target environment in mind. After all, a common chip can be found in a smartphone, a home automation device, and a scientific equipment, all of which run distinct operating systems.

The efficiency gains from adapting the compression-compilation design to the exact architecture on which a model is meant to operate can be considerable. Furthermore, popular devices already in the hands of customers can give the real-time AI experience that developers have been pursuing and touting for years. Models ranging from natural language to selfie filters can operate natively faster than they could on specialized hardware, requiring only a regular app install from the user. As a result, the next generation of AI can be implemented without the need for multibillion-dollar infrastructure investments.

For at least the next three years, there will be a chip shortage. But that doesn't rule out the possibility of an AI-powered future. Software-based solutions have helped us get to this stage in machine learning applications, and they may help us go much further if we use them correctly.

ABOUT THE WRITER

A keen observer, love to read geopolitics and investment strategies, writes on the impact of the global semiconductor chip shortage.