In early 2016, machine learning was still considered more of a scientific experiment than a practical tool. However, in just a few years, it has evolved into a powerful force across numerous industries. Today, it's being applied in areas such as data exploration, computer vision, natural language processing, biometrics, search engines, medical diagnosis, credit card fraud detection, stock market analysis, voice and handwriting recognition, strategy games, and robotics. The rapid growth of machine learning over the past few years has exceeded expectations.
According to DeloitteGlobal’s latest forecast report, by 2018, large and medium-sized enterprises were beginning to focus more on integrating machine learning into their operations. The number of machine learning projects deployed would double compared to 2017, and by 2020, they were expected to double again. This indicates a clear upward trend in adoption and investment.
Currently, the term "AI chip" is gaining popularity, with various types of chips being used to support machine learning tasks. These include GPUs, CPUs, FPGAs, ASICs, TPUs, and optical flow chips. According to Deloitte, in 2018, GPUs and CPUs remained the dominant choices for machine learning applications. The demand for GPUs reached around 500,000 units, while FPGAs saw over 200,000 units used in machine learning tasks. ASICs, though less common, had a demand of about 100,000 units.
Notably, Deloitte highlighted that by the end of 2018, more than 25% of data centers using hardware to accelerate machine learning would incorporate FPGAs and ASICs. This suggests that these specialized chips are becoming increasingly important in the field.
Early adopters of FPGA and ASIC acceleration primarily focused on inference tasks in machine learning. However, as the technology matures, these chips are also expected to play a role in training modules, further expanding their applications.
In 2016, global FPGA chip sales surpassed $4 billion, showing strong market interest. A 2017 report titled *"Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks"* suggested that in certain scenarios, FPGAs could outperform GPUs in terms of speed and computational power.
Today, cloud service providers like Amazon AWS and Microsoft Azure have started integrating FPGA technology into their platforms. Alibaba has also partnered with Intel to leverage the Xeon-FPGA platform for cloud applications. Intel has emphasized how FPGAs can be used to optimize cloud platforms, improving efficiency in machine learning, video, and audio encryption tasks.
Although ASICs are designed for specific tasks, they are widely used across the industry. In 2017, the total revenue from ASICs reached approximately $15 billion. Companies like Google have begun using custom ASICs for machine learning, and chips optimized for TensorFlow have already been released.
Deloitte believes that the combination of CPUs and GPUs has significantly advanced machine learning. If future FPGA and ASIC solutions can continue to improve processing speed, efficiency, and reduce costs, machine learning applications may experience another wave of explosive growth.
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