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

doi:10.1109/ACCESS.2022.3146413

Return

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

2022-01-01

Jooho Wang

Sungkyung Park

Chester Sungchung Park *

DOI：10.1109/ACCESS.2022.3146413

Original

Original request for help

Save

Abstract

En 中文

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 functional 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 execution time 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. The source code is available on the GitHub repository: https://github.com/SDL-KU/SDDGSim.

Keywords:

Hardware acceleration

Memory management

Convolutional neural networks

Bandwidth

Spatial databases

Registers

Power demand

Convolutional neural networks (CNNs)

data dependence graph

design space exploration (DSE)

hardware accelerators