RingCNN：利用代数稀疏环张量实现节能的基于CNN的计算成像
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging
摘要：
在人工智能时代，卷积神经网络（CNN）正在成为计算成像的强大技术。它们在从严重失真的图像中重建精细纹理方面表现出了卓越的质量，并有可能将下一代相机和显示器带入我们的日常生活。然而，CNN需要密集的计算能力来生成高分辨率视频，并且在渲染密集细节时无视传统的稀疏技术。因此，在规则稀疏性中找到新的可能性对于实现基于CNN的计算成像的大规模部署至关重要。在本文中，我们考虑了一种基本但尚未充分探索的方法-代数稀疏性-用于节能的CNN加速。我们建议基于环代数构建CNN模型，该环代数正确定义了n元组的乘法，加法和非线性。然后，本质稀疏性将立即跟随，例如，实值权重的数量减少n倍。我们定义了环代数的几种变体，并将其统一到一个建模框架RingCNN中，并在图像质量和硬件复杂性方面进行了比较。最重要的是，我们进一步设计了一个新的环代数，它使用组件乘积最小化复杂性，并使用定向ReLU实现最佳质量。最后，我们在两种设置中实现了加速器eRingCNN，n=2和4（稀疏度为50%和75%），采用40纳米技术，支持高达4K UHD 30 fps的高级降噪和超分辨率。布局结果表明，它们可以分别使用3.76 W和2.22 W提供等效的41 TOPS。与实值对应物相比，我们的n=2的环形卷积引擎实现了2.00倍的能效和2.08倍的面积效率，同时具有相似甚至更好的图像质量。当n=4时，能量和面积的效率增益进一步增加到3.84倍和3.77倍，PSNR下降0.11 dB。
英文摘要：
In the era of artificial intelligence, convolutional neural networks (CNNs) are emerging as a powerful technique for computational imaging. They have shown superior quality for reconstructing fine textures from badly-distorted images and have potential to bring next-generation cameras and displays to our daily life. However, CNNs demand intensive computing power for generating high-resolution videos and defy conventional sparsity techniques when rendering dense details. Therefore, finding new possibilities in regular sparsity is crucial to enable large-scale deployment of CNN-based computational imaging. In this paper, we consider a fundamental but yet well-explored approach -- algebraic sparsity -- for energy-efficient CNN acceleration. We propose to build CNN models based on ring algebra that defines multiplication, addition, and non-linearity for n-tuples properly. Then the essential sparsity will immediately follow, e.g. n-times reduction for the number of real-valued weights. We define and unify several variants of ring algebras into a modeling framework, RingCNN, and make comparisons in terms of image quality and hardware complexity. On top of that, we further devise a novel ring algebra which minimizes complexity with component-wise product and achieves the best quality using directional ReLU. Finally, we implement an accelerator, eRingCNN, in two settings, n=2 and 4 (50% and 75% sparsity), with 40 nm technology to support advanced denoising and super-resolution at up to 4K UHD 30 fps. Layout results show that they can deliver equivalent 41 TOPS using 3.76 W and 2.22 W, respectively. Compared to the real-valued counterpart, our ring convolution engines for n=2 achieve 2.00x energy efficiency and 2.08x area efficiency with similar or even better image quality. With n=4, the efficiency gains of energy and area are further increased to 3.84x and 3.77x with 0.11 dB drop of PSNR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
