In the last decade, scientists have shown tremendous progress in artificial intelligence and machine learning. Following it, the AI chip market is growing rapidly, and there are many different types of AI chips available. Similar to CPU’s GHz we can measure simplified speed of the AI chips using TOPS.
In addition, AI hardware manufacturers are pushing the boundaries of next-generation human-machine interaction and putting much effort into developing innovative solutions for future applications. Especially Chinese AI companies are now dominating the AI space, ranking above South Korea, Taiwan, and USA.
AI stands for Artificial Intelligence, which involves the development of computer systems capable of performing tasks that typically require human intelligence, such as decision-making, learning, and language comprehension. TOPS, on the other hand, stands for Tera Operations Per Second, and it quantifies the computing power of AI systems.
You may be wondering what the TOPS numbers mean. Let’s have a top-level look at AI TOPS to help you understand the concept.
Most of us might not be a fan of performance metrics that are abstract for evaluating computing capabilities. AI companies often boast about the speed of their products by referring to a metric like Trillion Operations per Second (TOP/s). This allows them to compare the different NPU (neural processing unit) architectures.
Further, TOPS is a metric that shows in a single number how many computing tasks an AI chip can handle in one second at 100% utilization. In terms of how many math-related problems the chip can solve in the shortest time.
Although TOPS does not distinguish between the type and quality of tasks the chip can process, for example, if one AI chip offers 5 TOPS while another offers 15 TOPS, then you assume that the second chip’s performance is triple that of the first.
Nevertheless, one AI Hardware could be optimized to handle certain tasks, or it could have multiple processing cores taking on different AI tasks and operations, so the comparison is not as simple as it looks.
The latest AI (artificial intelligence) methods rely upon computation at an unimaginable level two years ago. As a result, increasingly advanced AI chips/AI accelerators and related hardware are being formulated and deployed to match complicated Neural Network processing capabilities.
Google created its own AI hardware named TPU, and most of its applications like Gmail are currently running using TPU technology; later, Microsoft designed its own AI hardware, named NPU (Neural Processing) Unit, to handle the rapid growth increase in Bing and Azure services.
AI technology, like GPT chatbots, has made significant advancements in recent years.
GPT-3, the latest version of the GPT series, has around 175 billion parameters and can perform a range of tasks, such as writing articles, creating poetry, and even coding.
Researchers are also exploring ways to make AI more ethical, transparent, and trustworthy for widespread adoption in various industries.
Apple has introduced the A14 Bionic chip in 2020 and the 2nd Generation of M Series chip in 2022. Furthermore, M2 takes the industry-leading performance per watt of M1 even further with an 18 percent faster CPU, a 35 percent more powerful GPU, and a 40 percent faster Neural Engine1. It also delivers 50 percent more memory bandwidth than M1 and up to 24GB of fast unified memory. The Neural Engine can process up to 15.8 trillion operations per second, over 40 percent more than M1. A14 chipset is a next-gen 16-core Neural Engine with 11 TOPS of processing performance. (mixed new M2 and old A14?)
Moreover, Apple also designed the next-gen 16-Core Neural Engine. It is capable of processing 11 Trillion operations per second. Which is almost double an increase in machine learning performance and 10 times faster computing calculations (please provide link).
AI-capable chipsets
Apart from Apple, China’s Huawei, South Kora’s Samsung, and Taiwan’s Mediatek make AI-capable chipsets for their mobiles.
Qualcomm smartphones focused Snapdragon 865 promises 15 TOPS whilst 2nd Gen Snapdragon 8cx laptop chip performs 9 TOPS.
Huawei, the second-largest smartphone vendor in the world. It introduced the newest member of the AI Chipset family 8-core Kirin 990 5G. Huawei claims that it’s one of the most powerful AI chipsets in the world.
Samsung Exynos 990 has a dual-core neural processing unit paired with a DSP, featuring 15 TOPS. Samsung claims that its AI features allow smartphones to include “intelligent cameras, extended reality and virtual assistant” AI features. This includes camera scene recognition that improves image optimization.
Mediatek’s Dimensity 1000+ SoCs chipset, with 8-core processors manufactured using 7-nanometer process technology called APU3.0. Mediatek’s Dimensity assures 4.5- TOP AI performance to use with AI Assistant, AI Camera and OS-level needs.
In fact, TOPS cannot measure the real-world workload. Because in the real-world scenario, performance can be lower than the TOP value and it is good practice to test the chips before using them in your project.