DataMaestro: A Versatile and Efficient Data Streaming Engine Bringing Decoupled Memory Access To Dataflow Accelerators
Year: 2025
Type: preprint
Abstract: Deep Neural Networks (DNNs) have achieved remarkable success across various intelligent tasks but encounter performance and energy challenges in inference execution due to data movement bottlenecks. We introduce DataMaestro, a versatile and efficient data streaming unit that brings the decoupled access/execute architecture to DNN dataflow accelerators to address this issue. DataMaestro supports flexible and programmable access patterns to accommodate diverse workload types and dataflows, incorporates fine-grained prefetch and addressing mode switching to mitigate bank conflicts, and enables customizable on-the-fly data manipulation to reduce memory footprints and access counts. We integrate five DataMaestros with a Tensor Core-like GeMM accelerator and a Quantization accelerator into a RISC-V host system for evaluation. The FPGA prototype and VLSI synthesis results demonstrate that DataMaestro helps the GeMM core achieve nearly 100% utilization, which is 1.05-21.39x better than state-of-the-art solutions, while minimizing area and energy consumption to merely 6.43% and 15.06% of the total system.
Source: arXiv (Cornell University)
Authors: Yi, Xiaoling, Deng, Yunhao, Antonio, Ryan, Kong, Fanchen, Paim, Guilherme, Verhelst, Marian
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6979367175
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