Intel Chief Architect Raja Koduri and DeepMind Distinguished Engineer

Throughout the presentations on specific chips as well as the keynotes by Intel Chief Architect Raja Koduri and DeepMind Distinguished Engineer Dan Belov, several related themes about the future of microprocessors emerged.In his talk, Intel’s Koduri talked about “exploding heterogeneity” in the context of evolution from the megahertz era to the multi-core era to the architecture era. While we’ve always had specialized functional units for graphics, floating-point, networking tasks, and so forth, the overarching theme for x86 systems during the past few decades has been standardization around a common instruction set.

This dynamic arguably first really started to break down when GPUs, originally developed for graphics, became so important for running machine learning (ML) workloads. Machine learning operations are heavily dependent on linear algebra (such as multiplying together large matrices). This is compute-intensive but simple and is almost tailor-made for GPUs, which have been developed for years by companies like Nvidia for use as video cards.

But, today, in addition to specialized what jobs can you get with a computer science degree processors like Google’s Tensor Processing Units (TPU) and the increased use of field-programmable gate arrays (FPGA), microprocessor vendors are also enhancing their on-chip vector capabilities. (Vector computing, which dominated early supercomputers, applies repeated operations to a string of data. It’s very efficient for a narrow set of workloads and survives mostly as specialty operations on modern, more general-purpose scalar processors.)