To keep up with the growing computational requirements of machine learning workloads, many-core accelerators integrate an ever-increasing number of processing elements, putting the efficiency of memory and interconnect subsystems to the test. In this work, we present the design of a multicast-capable AXI crossbar, with the goal of enhancing data movement efficiency in massively parallel machine learning accelerators. We propose a lightweight, yet flexible, multicast implementation, with a modest area and timing overhead (12% and 6% respectively) even on the largest physically-implementable 16-to-16 AXI crossbar. To demonstrate the flexibility and end-to-end benefits of our design, we integrate our extension into an open-source 288-core accelerator. We report tangible performance improvements on a key computational kernel for machine learning workloads, matrix multiplication, measuring a 29% speedup on our reference system.