AI Chipset

AI chipset is a new generation of microprocessors, designed to fasten the AI applications using lesser energy while maintaining the efficiency. The chips are anticipated to shape the future of artificial intelligence and play an integral role in economic development. The AI chipsets have applications in smart homes, robotics, autonomous cars, and other smart electronic devices.

AI Chipset

Types of AI Chipset and Their Applications

AI chips are categorized into three different types. All of these types are useful for different tasks. The types of AI chips include GPU, FPGA, and AISC.

GPU(General Processing Unit)

Initially, GPU was designed to employ with image processing applications which benefited from parallel computation. Since 2012, GPUs started being used for AI training systems. Still, GPU is designed for general purpose computing and in some cases, it is used for AI interface. However, GPUs consumes high energy as compared with its other counterparts.

FPGA (Field-Programming Gate Arrays)

FPGAs are associated with improved efficiency as compared with GPUs which allows it to be used in AI interface. FPGA is treated as an alternative of GPU. This can be customized and reconfigured as per the requirement by using hardware description languages such as VHDL. The customizable nature of FPGA makes it flexible for constantly evolving structure of AI programs. Also, it provides high band-width memory and custom parallelism which makes it ideal for real-time interference model.

FPGA has gained traction in deep learning and machine learning, the resource hungry platforms. By using FPGA, high level of application can be ensured at low power machine learning.

Some of the companies started implementing cloud-base FPGA model for AI workload acceleration. For instance, Alibaba Cloud uses Intel’s Stratix 10 FPGA. Intel is one of the leading FPGA manufacturers in AI acceleration sector.

ASIC (Application Specific Integrated Circuit)

ASIC and GPU are very similar in technical manner. ASIC chips offer the facility to be programmed as an accelerator for instantaneous algorithms. These chips allow multiple AI algorithms to operate at the same time without compromising the computing power. These chips are anticipated to gain traction in forthcoming years owing to several advantages associated with it.

ASIC chips have major applications in testing and training of AI algorithms. AI algorithms can be accelerated faster on these chips as these chips are able to handle workload in parallelism.

Currently, only Google is the only major tech giant investing in ASIC chips. Recently, Google announced unveiled the second generation of TPU (Tensor Processing Unit), an ASIC chip based on TensorFlow technology.

Also read….

Artificial Intelligence | History of AI | Applications of Artificial Intelligence 

Open Source Technology |Advantages of Open Source Technology 

What is the Need for AI Chipset

AI chips are able to execute a larger volume of calculations simultaneously as compared with CPUs. Also, these chips reduce the number of transistors and calculate the numbers with low precision in a way that is fully compatible with AI algorithms. It stores entire algorithm into a single AI chip which speed up the memory access.

For interface and training of AI algorithms, specialized AI chips are more efficient and faster as compared with CPUs. Also, state-of-the-art chips are comparatively more cost efficient due to their greater efficiency for AI algorithms.

Now-a-days any major search, commerce, or social-networking website have an abundance of “deep-learning” algorithms. Since past few years, these dominant artificial intelligence (AI) tools have been increasingly and successfully applied to speech recognition, image analysis, translation, and many other functions. Without a doubt, the computational and power requirements of these algorithms now constitute a major and still-growing fraction of data center demand.

Owing to the fast-growing demand, however, companies are racing to develop hardware that more directly empowers deep learning, most urgently for inference but also for training. Most efforts focus on “accelerators” that, like GPUs, rapidly perform their specialized tasks under the loose direction of a general-purpose processor, although complete dedicated systems are also being explored.

With the increasing demand companies are focused on developing hardware that directly empower deep learning for training and interface. Furthermore, when it comes to the compute-cutting-edge AI, hardware vendors are reviving the performance gains we enjoyed at the height of Moore’s Law. The advantages come from a new generation of specialized chips for AI applications like deep learning. But the fragmented microchip marketplace that’s emerging will lead to some hard choices for developers.

AI researchers need every advantage they can get when dealing with the unprecedented computational requirements of deep learning. GPU processing power has advanced rapidly, and chips originally designed to render images have become the workhorses powering world-changing AI research and development.

Future of AI Chipset

Development and investment in AI chips are expected to increase significantly in near future as major use cases of artificial intelligence including AI-powered medical equipment and autonomous driving is anticipated to grow at a decent pace. Also increase in data volume will necessitate the requirement of better computing and algorithm.

According to an AI Index, between 2015 to 2018, the number of AI start-ups increased by 113% in United states. More than 45 start-ups are working on corresponding semiconductor solutions. Moreover, these start-ups are receiving noteworthy funding from investors. Some of the instances are stated below:

In March 2020, Hailo, a company specialized in deep learning announced that they have raised US$ 60 Million series B funding by ABB technology Venture, led by their existing investors. This funding will help the company to develop its new Hailo-8 Deep Learning chip.

In March 2020, a Silicon Valley and Shenzhen-based AI startup announced that they raised US$10 Million in angel round financing. Chinese venture capital firm Keytone Ventures led the investment round co-invested with Cloud Angel Fund and Creation Venture Partners.

In July 2019, NeuroBlade, an Israeli startup announced to complete Round A financing for US$ 23 Million. The new funding is intended to be used to develop the first generation of the NeuroBlade AI chip. The funding round was led by Intel Capital with previous investors Grove Ventures and StageOne Ventures. 

This is expected to boost the growth of AI chipset across the globe. Let me know your insights in comment box 🙂

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