There are many types of chips, such as graphics chips and processor chips. With the development of technology, one type of chip has emerged: the AI chip. To enhance everyone's understanding of AI chips, this article will explain them in detail. If you are interested in chips, feel free to continue reading with us!
Broadly speaking, any chip capable of running artificial intelligence algorithms is called an AI chip. More commonly, an AI chip refers to a chip specifically designed to accelerate artificial intelligence algorithms. AI chips are also known as AI accelerators or computing cards, meaning modules specifically designed to handle the large amounts of computation in artificial intelligence applications (other non-computational tasks are still handled by the CPU).
Phase 1: Due to insufficient chip computing power, neural networks were not given much attention;
Phase Two: The computing power of general-purpose CPU chips increased significantly, but it still could not meet the needs of neural networks;
Phase 3: GPUs and new-architecture AI chips drive the implementation of artificial intelligence.
The GPT-3 model has been selected as one of MIT Technology Review's "10 Breakthrough Technologies" for 2021. The largest dataset used by the GPT-3 model reached a size of 45TB before processing. According to OpenAI's computing power statistics unit petaflops/s-days, training AlphaGo Zero required 1800-2000 pfs-days, while GPT-3 used 3640 pfs-days.
AI computing refers to neural network algorithms, represented by "deep learning," which require systems capable of efficiently processing large amounts of unstructured data (text, video, images, audio, etc.). This necessitates hardware with efficient linear algebra computation capabilities. The computational tasks are characterized by simple unit computations and low logic control requirements, but high parallel computation volume and numerous parameters. This places high demands on chip multi-core parallel computing, on-chip storage, bandwidth, and low-latency memory access.
With the continuous breakthroughs in artificial intelligence technology over the past decade, large-scale commercial applications have been driven. As a crucial physical foundation for the large-scale application of artificial intelligence technology, AI chips also possess enormous industrial value and strategic importance.
AI chip applications are expanding into multiple dimensions, including computer vision (CV), autonomous driving, smartphones, and voice interaction. In the voice interaction field, the Chinese intelligent voice market is maintaining rapid growth. According to a Deloitte report, consumer-level applications are projected to exceed 70 billion yuan by 2030, while enterprise-level applications are expected to reach a scale of hundreds of billions of yuan.
To provide a better interactive experience for smart terminals, developing AI chips that match voice algorithms and adopting an integrated hardware and software solution is currently the most common choice in the industry, and it is also an inevitable path for technological iteration. Today, the intelligent voice field is crowded with many players, such as Baidu, iFlytek, Unisound, Speechocean, Mobvoi, and Qiying Tailun, all of whom have invested in the chip industry.
Intelligent voice technology inherently possesses greater complexity in terms of technological connections and data layers. The ability to understand and process natural language alone has consumed decades of scientists' efforts to achieve today's interactive experience. The reasonable path to commercializing this technology lies in achieving "cloud-chip" integration of technologies such as speech recognition, semantic understanding, natural language processing, speech synthesis, and noise reduction, extending the business to chips and even hardware.
It's a well-known fact that general-purpose chip architectures are not specifically designed for AI and inherently have limitations in performance and power consumption. In recent years, thanks to the efforts of many companies in the industry, the compatibility issues of traditional general-purpose chips have been resolved, and companies have also invested heavily in the manufacturing of application-specific chips.
Even after the technical issues are resolved, AI voice chips still face many challenges on the road to commercialization:
First, how to achieve optimal performance within cost constraints? Intelligent voice technology is tightly coupled; piecemeal solutions cannot achieve ideal interactive effects. A full-stack solution needs to be integrated onto the chip, but each additional function means increased cost. Low cost, ease of implementation, and low power consumption are essential product characteristics that must be closely integrated with the solution.
Secondly, looking at companies that have entered the AI voice chip market, their chosen application scenarios are concentrated in home, appliance, robotics, and automotive sectors. However, these scenarios are characterized by a wide variety of products, especially home appliances, where everything from a large air conditioner to a small socket requires a voice chip. Adapting the chip to these devices and determining the necessity of each chip's function requires a deep understanding of the functional know-how of the end products.
Third, due to the inherent dispersion of customer manufacturers, standard products combined with customized tools represent the most efficient collaboration model. Having an efficient toolchain, reducing the time and marginal costs required for customization, will significantly accelerate the commercialization of voice chips.
