We present a method that accelerates reconstruction of 3D scenes and objects, aiming to enable instant reconstruction on edge devices such as mobile phones and AR/VR headsets. While recent works have accelerated scene reconstruction training to minute/second-level on high-end GPUs, there is still a large gap to the goal of instant training on edge devices which is yet highly desired in many emerging applications such as immersive AR/VR. To this end, this work aims to further accelerate training by leveraging geometry priors of the target scene. Our method proposes strategies to alleviate the noise of the imperfect geometry priors to accelerate the training speed on top of the highly optimized Instant-NGP. On the NeRF Synthetic dataset, our work uses half of the training iterations to reach an average test PSNR of >30.

Vision Transformer (ViT) has emerged as a competitive alternative to convolutional neural networks for various computer vision applications. Specifically, ViT multi-head attention layers make it possible to embed information globally across the overall image. Nevertheless, computing and storing such attention matrices incurs a quadratic cost dependency on the number of patches, limiting its achievable efficiency and scalability and prohibiting more extensive real-world ViT applications on resource-constrained devices. Sparse attention has been shown to be a promising direction for improving hardware acceleration efficiency for NLP models. However, a systematic counterpart approach is still missing for accelerating ViT models. To close the above gap, we propose a first-of-its-kind algorithm-hardware codesigned framework, dubbed ViTALiTy, for boosting the inference efficiency of ViTs. Unlike sparsity-based Transformer accelerators for NLP, ViTALiTy unifies both low-rank and sparse components of the attention in ViTs. At the algorithm level, we approximate the dot-product softmax operation via first-order Taylor attention with row-mean centering as the low-rank component to linearize the cost of attention blocks and further boost the accuracy by incorporating a sparsity-based regularization. At the hardware level, we develop a dedicated accelerator to better leverage the resulting workload and pipeline from ViTALiTy's linear Taylor attention which requires the execution of only the low-rank component, to further boost the hardware efficiency. Extensive experiments and ablation studies validate that ViTALiTy offers boosted end-to-end efficiency (e.g., $3\times$ faster and $3\times$ energy-efficient) under comparable accuracy, with respect to the state-of-the-art solution.

Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resource-constrained applications. To tackle this limitation, pioneering works have developed handcrafted multiplication-free DNNs, which require expert knowledge and time-consuming manual iteration, calling for fast development tools. To this end, we propose a Neural Architecture Search and Acceleration framework dubbed NASA, which enables automated multiplication-reduced DNN development and integrates a dedicated multiplication-reduced accelerator for boosting DNNs' achievable efficiency. Specifically, NASA adopts neural architecture search (NAS) spaces that augment the state-of-the-art one with hardware-inspired multiplication-free operators, such as shift and adder, armed with a novel progressive pretrain strategy (PGP) together with customized training recipes to automatically search for optimal multiplication-reduced DNNs; On top of that, NASA further develops a dedicated accelerator, which advocates a chunk-based template and auto-mapper dedicated for NASA-NAS resulting DNNs to better leverage their algorithmic properties for boosting hardware efficiency. Experimental results and ablation studies consistently validate the advantages of NASA's algorithm-hardware co-design framework in terms of achievable accuracy and efficiency tradeoffs. Codes are available at https://github.com/GATECH-EIC/NASA.

Vision Transformers (ViTs) have achieved state-of-the-art performance on various vision tasks. However, ViTs' self-attention module is still arguably a major bottleneck, limiting their achievable hardware efficiency. Meanwhile, existing accelerators dedicated to NLP Transformers are not optimal for ViTs. This is because there is a large difference between ViTs and NLP Transformers: ViTs have a relatively fixed number of input tokens, whose attention maps can be pruned by up to 90% even with fixed sparse patterns; while NLP Transformers need to handle input sequences of varying numbers of tokens and rely on on-the-fly predictions of dynamic sparse attention patterns for each input to achieve a decent sparsity (e.g., >=50%). To this end, we propose a dedicated algorithm and accelerator co-design framework dubbed ViTCoD for accelerating ViTs. Specifically, on the algorithm level, ViTCoD prunes and polarizes the attention maps to have either denser or sparser fixed patterns for regularizing two levels of workloads without hurting the accuracy, largely reducing the attention computations while leaving room for alleviating the remaining dominant data movements; on top of that, we further integrate a lightweight and learnable auto-encoder module to enable trading the dominant high-cost data movements for lower-cost computations. On the hardware level, we develop a dedicated accelerator to simultaneously coordinate the enforced denser/sparser workloads and encoder/decoder engines for boosted hardware utilization. Extensive experiments and ablation studies validate that ViTCoD largely reduces the dominant data movement costs, achieving speedups of up to 235.3x, 142.9x, 86.0x, 10.1x, and 6.8x over general computing platforms CPUs, EdgeGPUs, GPUs, and prior-art Transformer accelerators SpAtten and Sanger under an attention sparsity of 90%, respectively.

神经体系结构搜索（NAS）在从给定的超网中寻找有效的深神经网络（DNN）方面取得了惊人的成功。同时，彩票票证假设表明，DNN包含可以从头开始训练的小子网，以达到比原始DNN的可比精度或更高的精度。因此，目前是通过第一次搜索然后修剪的管道开发有效的DNN的常见做法。然而，这样做通常需要进行搜索训练培训过程，因此计算成本过高。在本文中，我们首次发现高效的DNN及其彩票子网（即彩票）可以直接从超级网络中直接识别，我们将其称为超级票，这是通过共同体系结构的两合一培训方案。搜索和参数修剪。此外，我们制定了一种进步和统一的超级标识识别策略，该策略使子网络在超网训练期间的连通性更改，比传统的稀疏培训更高的准确性和效率折衷。最后，我们评估了从一个任务中汲取的这种确定的超级款项是否可以很好地转移到其他任务，从而验证其同时处理多个任务的潜力。对三个任务和四个基准数据集进行的广泛实验和消融研究表明，与典型的NAS和修剪管道相比，我们所提出的超级款项实现了提高的准确性和效率权衡。可以在https://github.com/rice-eic/supertickets上获得代码和预估计的模型。

有效的深层神经网络（DNN）模型配备了紧凑的操作员（例如，深度卷积）在降低DNN的理论复杂性（例如，权重/操作总数）的同时，在保持体面的模型准确性的同时，显示出很大的潜力。但是，由于其通常采用的紧凑型操作员的低硬件利用率，现有的有效DNN仍然受到履行其提高现实硬件效率的承诺的限制。在这项工作中，我们为开发真实硬件有效的DNN开辟了新的压缩范式，从而提高了硬件效率，同时保持模型的准确性。有趣的是，我们观察到，尽管某些DNN层的激活功能有助于DNNS的训练优化和可实现的准确性，但在训练后可以正确删除它们，而不会损害模型的准确性。受到这一观察的启发，我们提出了一个称为DepthShrinker的框架，该框架通过缩小现有有效DNN的基本构建块来开发硬件友好的紧凑型网络，这些构件具有不规则的计算模式，并具有大量改进的硬件利用率，从而将硬件的计算模式缩小到密集的情况下。令人兴奋的是，我们的DepthShrinker框架提供了硬件友好的紧凑网络，既优于最先进的有效DNN和压缩技术方法元元素。我们的代码可在以下网址找到：https：//github.com/facebookresearch/depthshrinker。

具有密集乘法的神经网络（NNS）（例如，卷积和变形金刚）具有饥饿的能力，阻碍了它们更广泛的部署到资源受限的设备中。因此，遵循节能硬件实施的共同实践的无乘法网络，以更有效的运算符（例如，位移位和加法）参数化NN，并引起了人们的关注。但是，从实现的准确性方面，无乘法网络的表现不足。为此，这项工作倡导混合NN，包括强大但昂贵的乘法和有效而强大的运营商来嫁给两全其美的运营商，并提出了ShiftAddnas，它们可以自动寻找更准确，更有效的NN。我们的ShiftAddnas突出了两个推动者。具体而言，它集成了（1）第一个混合搜索空间，该空间同时结合了基于乘法的和无乘法的运算符，以促进精确和有效的混合NNS的开发； （2）一种新型的重量共享策略，可以在遵循异质分布的不同操作员之间有效分享（例如，用于卷积的高斯与添加操作员的拉普拉斯人），并同时导致超级降低的超网尺寸和更好的搜索网络。对各种模型，数据集和任务的广泛实验和消融研究始终如一地验证了ShiftAddnas的功效，例如，与最先进的NN相比，获得的精度高达 +4.7％，或者+4.9更好的BLEU得分，而BLEU得分更好最多可提供93％或69％的能源和延迟节省。可以在https://github.com/rice-eic/shiftaddnas上获得代码和预估计的模型。

图表卷积网络（GCNS）已成为最先进的图形学习模型。但是，它可以令人难以置于大图数据集的推断GCNS，这会将其应用于大型实际图表并阻碍更深层更复杂的GCN图形的探讨。这是因为真实世界图可能非常大而稀疏。此外，GCN的节点度倾向于遵循幂律分布，因此具有高度不规则的邻接矩阵，导致数据处理和移动中的禁止低效率，从而显着地限制了可实现的GCN加速效率。为此，本文提出了一种GCN算法和加速器协同设计框架被称为GCOD，其在很大程度上可以缓解上述GCN不规则性并提高GCNS推理效率。具体地，在算法级别上，GCOD集成了分割和征服GCN训练策略，该训练策略将图形偏离在本地邻域中的密集或稀疏，而不会影响模型精度，从而导致（主要）的图形邻接矩阵仅仅是两个级别的工作量并享受大部分增强的规律性，从而轻松加速。在硬件水平上，我们进一步开发了一个具有分离发动机的专用双子加速器，以处理每个上述密集和稀疏工作负载，进一步提高整体利用率和加速效率。广泛的实验和消融研究验证了我们的GCOD始终如一地减少了与CPU，GPU和现有技术GCN加速器相比的15286倍，294倍，7.8倍和2.5倍的加速，包括HYGCN和AWB -GCN分别在保持甚至提高任务准确性的同时。

VITS通常太昂贵昂贵，无法安装在现实世界资源受限的设备上，因为（1）它们与输入令牌的数量和（2）其过度分开的自我关注头和模型深度相反的复杂性。并行地，不同的图像具有变化性变化，并且它们的不同区域可以包含各种级别的视觉信息，表明在模型复杂性方面同样地处理所有区域/令牌是不必要的，而这些机会尚未完全探索修剪vits的复杂性的机会。为此，我们提出了一种多粒子的输入 - 自适应视觉变压器框架被称为MIA-Fight，可以在三个粗粒细粒粒度（即，模型深度和模型数量的数量头/令牌）。特别是，我们的MIA-Agent采用具有混合监督和加固训练方法的低成本网络，以跳过不必要的层，头部和令牌以输入的自适应方式，降低整体计算成本。此外，我们的mia-ideor的有趣副作用是它的由此产生的vits自然地配备了对他们静态同行的对抗对抗攻击的改善的鲁棒性，因为米娅 - 以前的多粒度动态控制改善了模型多样性，类似于集合的效果因此，增加对抗所有子模型的对抗性攻击的难度。广泛的实验和消融研究验证了所提出的MIA - 前框架可以有效地分配适应性的计算预算与输入图像的难度增加，同时增加稳健性，实现最先进的（SOTA）精度效率权衡，例如20与SOTA动态变压器模型相比，％计算节省相同甚至更高的准确性。

神经结构搜索（NAS）已被广泛采用设计准确，高效的图像分类模型。但是，将NAS应用于新的计算机愿景任务仍然需要大量的努力。这是因为1）以前的NAS研究已经过度优先考虑图像分类，同时在很大程度上忽略了其他任务; 2）许多NAS工作侧重于优化特定于任务特定的组件，这些组件不能有利地转移到其他任务; 3）现有的NAS方法通常被设计为“Proxyless”，需要大量努力与每个新任务的培训管道集成。为了解决这些挑战，我们提出了FBNetv5，这是一个NAS框架，可以在各种视觉任务中寻找神经架构，以降低计算成本和人力努力。具体而言，我们设计1）一个简单但包容性和可转换的搜索空间; 2）用目标任务培训管道解开的多址搜索过程; 3）一种算法，用于同时搜索具有计算成本不可知的多个任务的架构到任务数。我们评估所提出的FBNetv5目标三个基本视觉任务 - 图像分类，对象检测和语义分割。 FBNETV5在单一搜索中搜索的模型在所有三个任务中都表现优于先前的议定书 - 现有技术：图像分类（例如，与FBNetv3相比，在与FBNetv3相比的同一拖鞋下的1 + 1.3％Imageet Top-1精度。 （例如，+ 1.8％较高的Ade20k Val。Miou比SegFormer为3.6倍的拖鞋），对象检测（例如，+ 1.1％Coco Val。与yolox相比，拖鞋的1.2倍的地图。