Systolic Array
A systolic array is a parallel computing architecture consisting of a grid of processing elements (PEs) that rhythmically compute and pass data through the system, similar to the systolic pumping of blood in the heart. It is designed for high-throughput, regular computations like matrix multiplication, convolution, and signal processing by enabling data to flow in a pipelined fashion across the array. This architecture minimizes memory access bottlenecks and is commonly implemented in hardware accelerators such as GPUs, TPUs, and FPGAs for AI and scientific computing tasks.
Developers should learn about systolic arrays when working on performance-critical applications involving dense linear algebra, neural network inference, or digital signal processing, as they offer significant speedups by exploiting data locality and parallelism. This concept is essential for optimizing hardware designs in AI accelerators (e.g., Google's TPU) or custom chips, where reducing memory bandwidth and latency is crucial for efficiency. Understanding systolic arrays helps in designing or utilizing specialized hardware for tasks like deep learning training, image processing, or scientific simulations.