IPUs

An IPU, or Intelligence Processing Unit, is a class of processor designed to accelerate artificial intelligence workloads. IPUs emphasize massive parallelism and flexible dataflow-style computation, differing from traditional CPUs and GPUs by mapping neural networks and related algorithms directly onto a large array of processing cores. The term IPU is most closely associated with Graphcore, a company that markets such devices and software as a platform for AI workloads.

Architecture: An IPU device comprises many independent cores arranged as tiles on a 2D grid. Each tile contains a programmable processor, local high-bandwidth memory, and a set of vector/matrix engines. Tiles connect through a high-speed on-chip network, enabling efficient communication and synchronization for large graphs. The design supports running large neural networks with irregular control flow and data-dependent operations, using a dataflow-style execution model rather than a fixed instruction stream.

Software and tools: IPUs are programmed with Graphcore's Poplar software stack, which compiles models to run on the IPU's graph-based execution. The ecosystem includes PyTorch bindings (PopTorch) and TensorFlow support, along with profiling and debugging tools such as PopVision visualizers for memory and performance.

Applications: IPUs are used for both training and inference of AI models, including natural language processing, computer vision, and recommendation systems. They are favored in workloads that can benefit from many small cores, large on-chip memory, and high inter-core bandwidth, particularly where models have dynamic control flow or sparse computations.

Considerations: While IPUs offer strong performance for certain AI workloads, they sit in a broader ecosystem that includes CPUs, GPUs, and other AI accelerators. Adoption may require specialized software and workflows, and performance depends on model architecture and software maturity.