DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection

Applying deep neural networks to 3D point cloud processing has attracted increasing attention due to its advanced performance in many areas, such as AR/VR, autonomous driving, and robotics. However, as neural network models and 3D point clouds expand in size, it becomes a crucial challenge to reduce the computational and memory overhead to meet latency and energy constraints in real-world applications. Although existing approaches have proposed to reduce both computational cost and memory footprint, most of them only address the spatial redundancy in inputs, i.e. removing the redundancy of background points in 3D data. In this paper, we propose a novel post-training weight pruning scheme for 3D object detection that is (1) orthogonal to all existing point cloud sparsifying methods, which determines redundant parameters in the pretrained model that lead to minimal distortion in both locality and confidence (detection distortion); and (2) a universal plug-and-play pruning framework that works with arbitrary 3D detection model. This framework aims to minimize detection distortion of network output to maximally maintain detection precision, by identifying layer-wise sparsity based on second-order Taylor approximation of the distortion. Albeit utilizing second-order information, we introduced a lightweight scheme to efficiently acquire Hessian information, and subsequently perform dynamic programming to solve the layer-wise sparsity. Extensive experiments on KITTI, Nuscenes and ONCE datasets demonstrate that our approach is able to maintain and even boost the detection precision on pruned model under noticeable computation reduction (FLOPs). Noticeably, we achieve over 3.89x, 3.72x FLOPs reduction on CenterPoint and PVRCNN model, respectively, without mAP decrease, significantly improving the state-of-the-art.

LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design

Recent literature has found that an effective method to customize or further improve large language models (LLMs) is to add dynamic adapters, such as low-rank adapters (LoRA) with Mixture-of-Experts (MoE) structures. Though such dynamic adapters incur modest computational complexity, they surprisingly lead to huge inference latency overhead, slowing down the decoding speed by 2.5+ times. In this paper, we analyze the fine-grained costs of the dynamic adapters and find that the fragmented CUDA kernel calls are the root cause. Therefore, we propose LoRA-Switch, a system-algorithm co-designed architecture for efficient dynamic adapters. Unlike most existing dynamic structures that adopt layer-wise or block-wise dynamic routing, LoRA-Switch introduces a token-wise routing mechanism. It switches the LoRA adapters and weights for each token and merges them into the backbone for inference. For efficiency, this switching is implemented with an optimized CUDA kernel, which fuses the merging operations for all LoRA adapters at once. Based on experiments with popular open-source LLMs on common benchmarks, our approach has demonstrated similar accuracy improvement as existing dynamic adapters, while reducing the decoding latency by more than 2.4 times.

Serving MoE Models on Resource-constrained Edge Devices via Dynamic Expert Swapping

Mixture of experts (MoE) is a popular technique in deep learning that improves model capacity with conditionally-activated parallel neural network modules (experts). However, serving MoE models in resource-constrained latency-critical edge scenarios is challenging due to the significantly increased model size and complexity. In this paper, we first analyze the behavior pattern of MoE models in continuous inference scenarios, which leads to three key observations about the expert activations, including temporal locality, exchangeability, and skippable computation. Based on these observations, we introduce PC-MoE, an inference framework for resource-constrained continuous MoE model serving. The core of PC-MoE is a new data structure, Parameter Committee, that intelligently maintains a subset of important experts in use to reduce resource consumption. The optimal configuration of Parameter Committee is found offline by a profiling-guided committee planner, and expert swapping and request handling at runtime are managed by an adaptive committee scheduler. To evaluate the effectiveness of PC-MoE, we conduct experiments using state-of-the-art MoE models on common computer vision and natural language processing tasks. The results demonstrate optimal trade-offs between resource consumption and model accuracy achieved by PC-MoE. For instance, on object detection tasks with the Swin-MoE model, our approach can reduce memory usage and latency by 42.34% and 18.63% with only 0.10% accuracy degradation.