Convolutional neural networks (CNNs) (including 2D and 3D convolutions) are popular in video analysis tasks such as action recognition and activity understanding. Fast algorithms such as fast Fourier transforms (FFTs) are promising in significantly reducing computation complexity by transforming convolution into frequency domain. In frequency space, conventional spatial convolutions are replaced with simpler element-wise complex multiplications. Conventional application-specific-integrated-circuit (ASIC) based frequency-domain accelerators can achieve effective performance boost but come at the cost of significant energy consumption, owing to the hierarchical memory organization. We propose a frequency-domain resistive random access memory (ReRAM) based inference accelerator called FDA that can process element-wise complex multiplication in memory for both 2D and 3D CNNs. Each ReRAM-based frequency-domain process element (PE) with two ReRAM cells can perform an element-wise complex multiplication in two continuous execution cycles. We then provide a flexible dataflow to alleviate the redundant data movements by frequency-domain data reuse and inherent symmetrical characteristic for both 2D and 3D convolutions. Evaluation results based on representative both 2D and 3D CNN benchmarks demonstrate that FDA outperforms state-of-the-art baselines with better performance and energy efficiency.

3D convolutional neural networks (CNNs) are widely deployed in video analysis. Fast algorithms such as fast Fourier transforms (FFTs) are gaining popularity in reducing computation complexity for their superior capability of replacing convolutions with simpler element-wise multiplications. Conventional frequency-domain dedicated accelerators employ memory hierarchy organization for high throughput but at the expensive costs of a significant amount of data movements and energy consumptions. This paper presents F3D, a processingin-memory frequency-domain accelerator using resistive random access memory (ReRAM). F3D supports frequency-domain complex number multiplications directly in ReRAM-based crossbar architecture. We alleviate the overheads of redundant data movements in ReRAM-based complex number multiplications by data reuse and the inherent symmetry of inputs in the frequency space. Evaluation results demonstrate that F3D outperforms state-of-the-art accelerators with significant improvements in performance and energy efficiency.