Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory size. We find that the memory bottleneck is due to the imbalanced memory distribution in convolutional neural network (CNN) designs: the first several blocks have an order of magnitude larger memory usage than the rest of the network. To alleviate this issue, we propose a generic patch-by-patch inference scheduling, which operates only on a small spatial region of the feature map and significantly cuts down the peak memory. However, naive implementation brings overlapping patches and computation overhead. We further propose network redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead. Manually redistributing the receptive field is difficult. We automate the process with neural architecture search to jointly optimize the neural architecture and inference scheduling, leading to MCUNetV2. Patch-based inference effectively reduces the peak memory usage of existing networks by 4-8x. Co-designed with neural networks, MCUNetV2 sets a record ImageNet accuracy on MCU (71.8%) and achieves >90% accuracy on the visual wake words dataset under only 32kB SRAM. MCUNetV2 also unblocks object detection on tiny devices, achieving 16.9% higher mAP on Pascal VOC compared to the state-of-the-art result. Our study largely addressed the memory bottleneck in tinyML and paved the way for various vision applications beyond image classification.
In MCUNetV2, we propose a generic patch-by-patch inference scheduling, which operates only on a small spatial region of the feature map and significantly cuts down the peak memory. We further propose network redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead.
Deep learning on point clouds plays a vital role in a wide range of applications such as autonomous driving and AR/VR. These applications interact with people in real time on edge devices and thus require low latency and low energy. Compared to projecting the point cloud to 2D space, directly processing 3D point cloud yields higher accuracy and lower #MACs. However, the extremely sparse nature of point cloud poses challenges to hardware acceleration. For example, we need to explicitly determine the nonzero outputs and search for the nonzero neighbors (mapping operation), which is unsupported in existing accelerators. Furthermore, explicit gather and scatter of sparse features are required, resulting in large data movement overhead. In this paper, we comprehensively analyze the performance bottleneck of modern point cloud networks on CPU/GPU/TPU. To address the challenges, we then present PointAcc, a novel point cloud deep learning accelerator. PointAcc maps diverse mapping operations onto one versatile ranking-based kernel, streams the sparse computation with configurable caching, and temporally fuses consecutive dense layers to reduce the memory footprint. Evaluated on 8 point cloud models across 4 applications, PointAcc achieves 3.7X speedup and 22X energy savings over RTX 2080Ti GPU. Codesigned with light-weight neural networks, PointAcc rivals the prior accelerator Mesorasi by 100X speedup with 9.1% higher accuracy running segmentation on the S3DIS dataset. PointAcc paves the way for efficient point cloud recognition.
PointAcc is a novel point cloud deep learning accelerator. It introduces a configurable sorting-based mapping unit that efficiently supports diverse operations in point cloud networks. PointAcc further exploits simplified caching and layer fusion specialized for point cloud models, effectively reducing the DRAM access.
Multi-sensor solution has been widely adopted in real-world robotics systems (e.g., self-driving vehicles) due to its better robustness. However, its performance is highly dependent on the accurate calibration between different sensors, which is very time-consuming (i.e., hours of human efforts) to acquire. Recent learning-based solutions partially address this yet still require costly ground-truth annotations as supervision. In this paper, we introduce a novel self-supervised semantic alignment loss to quantitatively measure the quality of a given calibration. It is well correlated with conventional evaluation metrics while it does not require ground-truth calibration annotations as the reference. Based on this loss, we further propose an annotation-free optimization-based calibration algorithm (SemAlign) that first estimates a coarse calibration with loss-guided initialization and then refines it with gradient-based optimization. SemAlign reduces the calibration time from hours of human efforts to only seconds of GPU computation. It not only achieves comparable performance with existing supervised learning frameworks but also demonstrates a much better generalization capability when transferred to a different dataset.
Multi-sensor fusion is important in real-world robotics systems, but aligning different sensors through calibration is challenging and requires hours of human efforts. To this end, we propose SemAlign that does not require ground-truth calibration annotations and automates the process of camera-3D calibration.
Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing. However, due to the high computational cost of large-scale generators (e.g., StyleGAN2), it usually takes seconds to see the results of a single edit on edge devices, prohibiting interactive user experience. In this paper, inspired by quick preview features in modern rendering software, we propose Anycost GAN for interactive natural image editing. We train the Anycost GAN to support elastic resolutions and channels for faster image generation at versatile speeds. Running subsets of the full generator produce outputs that are perceptually similar to the full generator, making them a good proxy for quick preview. By using sampling-based multi-resolution training, adaptive-channel training, and a generator-conditioned discriminator, the anycost generator can be evaluated at various configurations while achieving better image quality compared to separately trained models. Furthermore, we develop new encoder training and latent code optimization techniques to encourage consistency between the different sub-generators during image projection. Anycost GAN can be executed at various cost budgets (up to 10× computation reduction) and adapt to a wide range of hardware and latency requirements. When deployed on desktop CPUs and edge devices, our model can provide perceptually similar previews at 6-12× speedup, enabling interactive image editing.
Anycost GAN generates consistent outputs under various, fine-grained computation budgets.
