RRAM-Based Spectral-Domain Convolution Accelerator for Reliable and Energy-Efficient CNN Inference
RRAM-Based Spectral-Domain Convolution Accelerator for Reliable and Energy-Efficient CNN Inference
Abstract
The growing computational demands of convolutional neural networks (CNNs) have motivated the use of spectral-domain inference as an alternative to costly spatialdomain convolutions. In this work, we propose a resistive RAM (RRAM)-based spectral-domain convolutional layer that exploits in-memory computing (IMC) for low energy consumption and high parallelism. Both the 2-D Fourier transform and the elementwise multiplications are directly executed on RRAM crossbar arrays, while Hermitian symmetry is leveraged to further enhance the energy efficiency of the transform and subsequent spectral processing. To ensure robustness, the measured RRAM device data are incorporated into system-level simulations to evaluate inference accuracy under the impact of device variability. Furthermore, we introduce a layer-wise mapping framework that adaptively selects between spatial- and spectraldomain execution based on the tradeoff between energy efficiency and accuracy. Simulation results show that the proposed design achieves up to a 2.18× improvement in energy efficiency across various convolutional layer configurations compared with the spatial-domain design. For VGG-8 on CIFAR-100, the proposed architecture with the layer-wise mapping scheme reduces the energy-delay product (EDP) by 45% while incurring negligible accuracy loss. This work presents the first complete RRAM-based spectral-domain convolutional layer that accounts for device variability, providing a promising solution for edge CNN inference.
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