Artificial intelligence (AI), machine learning, and deep learning are buzz words in the tech world. What’s more, deep learning is regarded as the most successful paradigms of AI applications in the real world. Thus, when it comes to accelerating deep learning, companies turn toward deep learning chips and more than 80 startups and well-known tech giants that invest in this business.
According to Allied Market Research, the global deep learning chip market is expected to reach $29.36 billion by 2025. An increase in demand for smart homes & smart cities, the advent of quantum computing, and a surge in investments in artificial intelligence startups drive the market growth.
Developments in the deep learning chip industry
Let’s turn back time when Nvidia first launched its high-end general-purpose computing on GPUs, the world witnessed its first step toward deep learning. Since then, several companies have invested a colossal amount of money to design an architecture that could run AI and deep learning workloads. Today, several companies are in the race to launch better-than-all deep learning chips.
One of the prominent competitors in the deep learning chip market is IBM. The American multinational technology company has recently unveiled the world’s first AI accelerator chip. The chip is developed on seven-nanometer technology for offering a high level of energy efficiency.
The company launched the chip at the International Solid-State Circuits Virtual Conference. Although the technology is still at the research state, the chip is expected to be superior to existing deep learning chips and capable of supporting multiple AI models while achieving edge power efficiency.
The Consumer Technology Association (CES), the world’s most look-forward technology event is one chance to look at the disrupting technologies that could lead the future. During this year’s event Syntiant, a prominent name in the AI and machine learning world introduced its neural decision processor 120 (NPD120). The company received the Innovation Award for the design of the chip. The chip is the first step toward hardware-based AI and could be used for audio and sensor processing in battery-powered applications.
This deep learning chip runs at under 1 milliwatt and could handle several applications at the same time in consumer electronics devices including earbuds, smartphones, laptops, virtual assistants, security devices, and home entertainment devices. In addition, the chip offers echo-cancelation, noise suppression, beamforming, speech enhancements, keyword spotting, event detection, speaker identification, and local command recognition. The chip can recognize several wake words as well. What’s more, NDP120 can offer passive infrared detection and supports gyroscope, accelerometer, magnetometer, multi-sensor fusion, and pressure sensing MEMS applications.
Over the last couple of years, cloud companies have understood that AI lacks efficiency and versatility in more and one way. Recently, Google pointed out that there are other ways to optimize the architecture of chips and improve the performance of AI chips.
Google launched Apollo, one of the research projects that represent a new approach to designing AI chips. According to Jeff Dean, a Google Brain director, machine learning could offer insights on low-level design decisions and chip designers could use AI to determine the layout of circuits that form the chip’s operation. In simple words, Google aims to use AI to improve the architecture of upcoming deep learning chips. This way, there is a higher margin for improving the existing performance of the AI chips.
The launch of new deep learning chips and innovative ways to improve the architecture of AI chips have increased the market competition to develop higher performance, faster deep-learning chips. In the future, researchers would leverage the benefits of AI to improve their designs and AI-driven algorithms would become normalcy.
While the aim of AI is to develop human-like artificial brains, we are far behind in the reality. However, such AI-driver algorithms would put us closer to the ultimate goal.
Future of deep learning chip industry
Today every smartphone, laptop, and desktops seem to run a small set of chips that are quite similar to each other. However, there was a time when high-performance, high-intensity graphics workload proliferated GPUs were something new in the computing world. The chip industry has evolved a lot since then.
The rise of deep learning disrupted the chip industry and the “need for speed” become more immediate than ever. Deep learning refers to more complex and specialized workloads that no available chip could handle and companies found no other way but to design a dedicated chip with one purpose- deep learning.
From Google’s TPUs to Amazon’s Inferential chip, it is now a requirement for cloud companies to design their deep learning chip to maintain their presence in the market and this trend is here to stay.
The experts in the AI industry believe that the era of deep learning is just begun. While deep learning mimics the way the human brain works, we are still miles behind construction artificial brains that can perform a variety of functions with versatility and efficiency similar to the human brain. in the future, companies would focus on improving existing technology and advance in the field of deep learning.
There are endless challenges when it comes to developing artificial neural networks but the advent of contrastive learning technology could offer new opportunities. Thus, the demand for improved and high-performance deep learning chips would witness exponential growth in the future.
The world is leveraging and understanding the capabilities of AI simultaneously. The cloud companies aim to dump their workload on AI and building their AI empires on the cloud literally driving the deep learning world.
The applications of deep learning in the real world would only increase in the future. It has already become a multi-billion industry for deep learning chips. With the advent of 5G and AI hardware accelerators, the whole game of AI would change in the future. With the AI taking the load of computing as well as designing futuristic AI chips, only time would talk about the supremacy of deep learning chips.