  Deep neural network (DNN) models continue to grow in size and complexity,
demanding higher computational power to enable real-time inference. To
efficiently deliver such computational demands, hardware accelerators are being
developed and deployed across scales. This naturally requires an efficient
scale-out mechanism for increasing compute density as required by the
application. 2.5D integration over interposer has emerged as a promising
solution, but as we show in this work, the limited interposer bandwidth and
multiple hops in the Network-on-Package (NoP) can diminish the benefits of the
approach. To cope with this challenge, we propose WIENNA, a wireless NoP-based
2.5D DNN accelerator. In WIENNA, the wireless NoP connects an array of DNN
accelerator chiplets to the global buffer chiplet, providing high-bandwidth
multicasting capabilities. Here, we also identify the dataflow style that most
efficienty exploits the wireless NoP's high-bandwidth multicasting capability
on each layer. With modest area and power overheads, WIENNA achieves 2.2X--5.1X
higher throughput and 38.2% lower energy than an interposer-based NoP design.
