An Efficient Hybrid Deep Learning Accelerator for Compact and Heterogeneous CNNs
Artikel i vetenskaplig tidskrift, 2024
We propose FiBHA (Fixed Budget Hybrid CNN Accelerator), a hybrid accelerator composed of a single-engine single-layer Layer part and a single-engine multiple-layer part, each processing a subset of CNN layers. FiBHA captures more heterogeneity than single-engine multiple-layer while being more resource-aware and scalable than single-engine single-layer. Moreover, we propose a novel module, Fused Inverted Residual Bottleneck (FIRB), a fine-grained and memory-light single-engine single-layer architecture building block. The proposed architecture is implemented and evaluated using high-level synthesis (HLS) on different FPGAs representing various resource budgets. Our evaluation shows that FiBHA improves the throughput by up to 4x and 2.5x compared to state-of-the-art single-engine single-layer and single-engine multiple-layer accelerators, respectively. Moreover, FiBHA reduces memory and energy consumption compared to a single-engine multiple-layer accelerator. The evaluation also shows that FIRB reduces the required memory by up to 54%, and energy requirements by up to 35% compared to traditional pipelining.
hardware software co-design
FPGA
Convolutional neural networks (CNNs)
pipelined accelerator
deep learning
hybrid accelerator
Författare
Fareed Mohammad Qararyah
Chalmers, Data- och informationsteknik, Datorteknik
Muhammad Waqar Azhar
Chalmers, Data- och informationsteknik, Datorteknik
Pedro Petersen Moura Trancoso
Chalmers, Data- och informationsteknik, Datorteknik
Transactions on Architecture and Code Optimization
1544-3566 (ISSN) 1544-3973 (eISSN)
Vol. 21 2 25Very Efficient Deep Learning in IOT (VEDLIoT)
Europeiska kommissionen (EU) (EC/H2020/957197), 2020-11-01 -- 2023-10-31.
Ämneskategorier
Annan teknik
Datavetenskap (datalogi)
DOI
10.1145/3639823