Why Google's custom AI chips are shaking up the tech industry Ironwood is Google's latest tensor processing unit Nvidia's position as the dominant supplier of AI chips may be under threat from a specialised chip pioneered by Google, with reports suggesting companies like Meta and Anthropic are looking to spend billions on Google's tensor processing units. The success of the artificial intelligence industry has been in large part based on graphical processing units (GPUs), a kind of computer chip that can perform many parallel calculations at the same time, rather than one after the other like the computer processing units (CPUs) that power most computers. 'Flashes of brilliance and frustration': I let an AI agent run my day GPUs were originally developed to assist with computer graphics, as the name suggests, and gaming. "If I have a lot of pixels in a space and I need to do a rotation of this to calculate a new camera view, this is an operation that can be done in parallel, for many different pixels," says Francesco Conti at the University of Bologna in Italy. This ability to do calculations in parallel happened to be useful for training and running AI models, which often use calculations involving vast grids of numbers performed at the same time, called matrix multiplication.

This week is the eighth annual International Workshop on OpenCL, SYCL, Vulkan, and SPIR-V, and the event is available online for the very first time in its history thanks to the coronavirus pandemic. One of the event organizers, and the conference chair, is Simon McIntosh-Smith, who is a professor of high performance computing at Bristol University in Great Britain and also the head of its Microelectronics Group. Among other things, McIntosh-Smith was a microprocessor architect at STMicroeletronics, where he designed SIMD units for the dual-core, superscalar Chameleon and SH5 set-top box ASICs back in the late 1990s. McIntosh-Smith moved to Pixelfusion in 1999, which created the first general purpose GPU – arguably eight or nine years before Nvidia did it with its Tesla GPUs, where he was an architect on the 1,536-core chip and software manager for two years. In 2002, McIntosh-Smith was one of the co-founders of ClearSpeed, which created floating point math accelerators used in HPC systems before GPU accelerators came along, and was first director of architecture and applications and then vice president of applications.