{"id":111110,"date":"2023-10-10T13:06:59","date_gmt":"2023-10-10T13:06:59","guid":{"rendered":"https:\/\/www.techopedia.com"},"modified":"2023-10-12T17:39:43","modified_gmt":"2023-10-12T17:39:43","slug":"ai-explosion-fuels-demand-for-customized-chips","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/ai-explosion-fuels-demand-for-customized-chips","title":{"rendered":"AI Explosion Fuels Demand for Customized Chips"},"content":{"rendered":"
The artificial intelligence<\/a> (AI) market is booming, leading to a massive rise in the demand for customized chips.<\/p>\n However, the supply isn’t matching the demand, far from it.<\/p>\n Custom chips in devices such as smartphones and computers are now joined by more needs \u2014 from\u00a0face-recognition<\/a> hardware to AI to the Internet of Things<\/a> (IoT).<\/p>\n You can usually divide chips into two categories: general and custom. The general chips are those manufactured by companies such as Intel and AMD, and they cater to multiple use cases such as image processing<\/a> and multi-threading<\/a>.<\/p>\n Synopsis in a blog post places it like this:<\/p>\n \u201cAI workloads are massive, demanding a significant amount of bandwidth and processing power. As a result, AI chips require a unique architecture consisting of the optimal processors, memory arrays, security and real-time data connectivity.<\/p>\n Traditional CPUs<\/a> typically lack the processing performance needed, but are ideal for performing sequential tasks. GPUs, on the other hand, can handle the massive parallelism of AI\u2019s multiply-accumulate functions and can be applied to AI applications. GPUs can serve as AI accelerators, enhancing performance for neural networks<\/a> and similar workloads\u201d.<\/p><\/blockquote>\n The rise of generative AI<\/a> has been one of the main factors behind the increased demand for custom chips. Generative AI tools, which have exploded in the last year<\/a>, can generate custom content in the form of text, images, video, or other media in response to prompts<\/a>.<\/p>\n Organizations like Amazon, Microsoft, and Google realize that custom chips are critical for generative AI and have been focusing on developing in-house custom chips, particularly when the dominant player, NVidia, has already sold out until 2024<\/a>.<\/p>\n While companies like Amazon join the fray with chips like Inferentia<\/a>, many startups have been frantically working on developing chips.<\/p>\n For example, D-Matrix is a startup that raised $110 million to develop an inference-computing platform. According to Playground Global partner Sasha Ostojic, who supports D-Matrix, “D-Matrix is the company that will make generative AI commercially viable<\/a>.”<\/p>\n