Artificial Intelligence Chipset Market Trends

Key Emerging Trends in the Artificial Intelligence Chipset Market

Artificial Intelligence (AI) has permeated numerous applications, revolutionizing industries such as surgery, medical diagnoses, smart homes, criminal identification, water quality monitoring, self-driving cars, drug discovery, and image processing. The implementation of AI in diverse sectors necessitates compatible hardware and software components with high computational power and low energy consumption. AI chipsets, including Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), play a pivotal role in providing the requisite computing power for these applications on various devices.

To meet the growing demand for AI applications, manufacturers of AI chipsets continually introduce innovative processors. For instance, on August 9, 2016, Intel Corporation made a significant move by acquiring Nervana Systems, an AI software provider. This acquisition aimed to bolster Intel's deep learning portfolio of cloud services. Subsequently, on December 6, 2017, Intel announced the launch of the Intel Nervana Neural Network Processor (NNP) architecture, designed to facilitate the testing and deployment of AI models. The NNP architecture empowers designers to iterate neural networks efficiently with large datasets, providing flexibility for software to manage data locally, thus reducing the need for data movement to and from external memory and resulting in power savings.

The training of deep learning networks involves intricate data movement, and the Nervana Engine chip, a part of the architecture, employs high bandwidth memory (HBM) technology, offering 32 GB of on-chip storage with access speeds of up to 8 TB/s. Importantly, the Nervana NNP optimizes the training time for deep learning models by eliminating a hierarchy of cache memories, allowing software to handle on-chip memory management. This innovative design enables bi-directional data transfer and large network computations on a single chip with reduced power consumption per computation.

AI chipsets are not limited to cloud services and data centers; they have also found applications in smartphones, contributing to features such as image stabilization and overall performance improvement. For example, Apple Inc. integrated augmented reality and facial recognition features into its iPhone X series through the deployment of the A11 Bionic chip, showcasing the versatile applications of AI chipsets.

The diverse applications of AI across various industries are propelling the growth of the global artificial intelligence (AI) chipset market. The continuous evolution of AI chipsets, their integration into a wide array of devices, and their role in enhancing computational efficiency underscore their significance in the ever-expanding landscape of artificial intelligence. As industries continue to embrace AI-driven solutions, the market for AI chipsets is poised for sustained growth and innovation.