Spatial Data Dependence Graph Simulator for Convolutional Neural Network Accelerators

Wang, Jooho; Kim, Jiwon; Sungmin, Moon; Kim, Sunwoo; Park, Sungkyung; Park, Chester Sungchung

doi:10.1109/aicas.2019.8771561

Cited by 2 publications

(1 citation statement)

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“…Section VI verifies the estimated results obtained for the SDDG simulator, using the previously implemented hardware accelerator results. In addition, we estimate the PPA results for the hardware accelerators with respect to the dataflows and local memory constraints, using the DSE results of the proposed pre-RTL simulator; in particular, this section presents the extended simulation results given in previous studies [41]. Finally, Section VII describes the conclusions and future work.…”

Section: Introductionmentioning

confidence: 99%

Spatial Data Dependence Graph Based Pre-RTL Simulator for Convolutional Neural Network Dataflows

Wang

Park

2022

IEEE Access

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In this paper, a new pre-RTL simulator is proposed to predict the power, performance, and area of convolutional neural network (CNN) dataflows prior to register-transfer-level (RTL) design. In the simulator, a novel approach is adopted to implement a spatial data dependence graph (SDDG), which enables us to model a specific dataflow alongside inter-instruction dependencies by tracking the status of each processing element (PE). In addition, the proposed pre-RTL simulator makes it possible to evaluate the impact of memory constraints such as latency and bandwidth. The latency-insensitive and bandwidth-insensitive PE controllers assumed in the proposed pre-RTL simulator guarantee both operational correctness and maximum performance, regardless of memory constraints. In particular, it is shown that the optimal distribution method of local memory bandwidth can reduce the accelerator performance by up to 37.6% compared with the equal distribution method. For weight stationary (WS) and row stationary (RS) dataflows, the accelerator performance closely depends on memory constraints. The simulation results also show that the relative performances of dataflows depend on the layer shape of the convolutional layer. For example, for an identical hardware area in a standard convolutional layer of AlexNet, WS dataflows do not provide any performance gain over RS dataflows when the memory latency is sufficiently high. In addition, WS dataflows cannot fully reuse the input activation, thereby increasing local memory accesses, since the number of weights loaded at a specific time is limited. Moreover, in a depth-wise convolutional layer of MobileNet, WS dataflows tend to outperform RS dataflows even in the presence of large memory latency.

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Section: Introductionmentioning

confidence: 99%