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- W3106930119 abstract "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." @default.
- W3106930119 created "2020-12-07" @default.
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- W3106930119 date "2020-11-30" @default.
- W3106930119 modified "2023-10-03" @default.
- W3106930119 title "Dataflow-Architecture Co-Design for 2.5D DNN Accelerators using Wireless Network-on-Package" @default.
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