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1. Scalable Gaussian Process Variational Autoencoders

   Jazbec, M.; Ashman, M.; Fortuin, V.; Pearce, M.; Mandt, S.; Rätsch, G. (September 2021, nternational Conference on Artificial Intelligence and Statistics)

   null (Ed.)

   Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GPVAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference. We improve the scalability of these methods through principled sparse inference approaches. We propose a new scalable GPVAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components. 

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2. Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents

   https://doi.org/10.24963/ijcai.2019/466  

   Sarkhel, Ritesh; Nandi, Arnab (August 2019, 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019)

   Classifying heterogeneous visually rich documents is a challenging task. Difficulty of this task increases even more if the maximum allowed inference turnaround time is constrained by a threshold. The increased overhead in inference cost, compared to the limited gain in classification capabilities make current multi-scale approaches infeasible in such scenarios. There are two major contributions of this work. First, we propose a spatial pyramid model to extract highly discriminative multi-scale feature descriptors from a visually rich document by leveraging the inherent hierarchy of its layout. Second, we propose a deterministic routing scheme for accelerating end-to-end inference by utilizing the spatial pyramid model. A depth-wise separable multi-column convolutional network is developed to enable our method. We evaluated the proposed approach on four publicly available, benchmark datasets of visually rich documents. Results suggest that our proposed approach demonstrates robust performance compared to the state-of-the-art methods in both classification accuracy and total inference turnaround. 

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3. HgPCN: A Heterogeneous Architecture for E2E Embedded Point Cloud Inference

   https://doi.org/10.1109/MICRO61859.2024.00116  

   Gao, Yiming; Jiang, Chao; Piard, Wesley; Chen, Xiangru; Patel, Bhavesh; Lam, Herman (November 2024, IEEE)

   Point cloud is an important type of geometric data structure for many embedded applications such as autonomous driving and augmented reality. Current Point Cloud Networks (PCNs) have proven to achieve great success in using inference to perform point cloud analysis, including object part segmentation, shape classification, and so on. However, point cloud applications on the computing edge require more than just the inference step. They require an end-to-end (E2E) processing of the point cloud workloads: pre-processing of raw data, input preparation, and inference to perform point cloud analysis. Current PCN approaches to support end-to-end processing of point cloud workload cannot meet the real-time latency requirement on the edge, i.e., the ability of the AI service to keep up with the speed of raw data generation by 3D sensors. Latency for end-to-end processing of the point cloud workloads stems from two reasons: memory-intensive down-sampling in the pre-processing phase and the data structuring step for input preparation in the inference phase. In this paper, we present HgPCN, an end-to-end heterogeneous architecture for real-time embedded point cloud applications. In HgPCN, we introduce two novel methodologies based on spatial indexing to address the two identified bottlenecks. In the Pre-processing Engine of HgPCN, an Octree-Indexed-Sampling method is used to optimize the memory-intensive down-sampling bottleneck of the pre-processing phase. In the Inference Engine, HgPCN extends a commercial DLA with a customized Data Structuring Unit which is based on a Voxel-Expanded Gathering method to fundamentally reduce the workload of the data structuring step in the inference phase. The initial prototype of HgPCN has been implemented on an Intel PAC (Xeon+FPGA) platform. Four commonly available point cloud datasets were used for comparison, running on three baseline devices: Intel Xeon W-2255, Nvidia Xavier NX Jetson GPU, and Nvidia 4060ti GPU. These point cloud datasets were also run on two existing PCN accelerators for comparison: PointACC and Mesorasi. Our results show that for the inference phase, depending on the dataset size, HgPCN achieves speedup from 1.3× to 10.2× vs. PointACC, 2.2× to 16.5× vs. Mesorasi, and 6.4× to 21× vs. Jetson NX GPU. Along with optimization of the memory-intensive down-sampling bottleneck in pre-processing phase, the overall latency shows that HgPCN can reach the real-time requirement by providing end-to-end service with keeping up with the raw data generation rate. 

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4. InferDB: In-Database Machine Learning Inference Using Indexes

   https://doi.org/10.14778/3659437.3659441  

   Salazar-Díaz, Ricardo; Glavic, Boris; Rabl, Tilmann (April 2024, Proceedings of the VLDB Endowment)

   The performance of inference with machine learning (ML) models and its integration with analytical query processing have become critical bottlenecks for data analysis in many organizations. An ML inference pipeline typically consists of a preprocessing workflow followed by prediction with an ML model. Current approaches for in-database inference implement preprocessing operators and ML algorithms in the database either natively, by transpiling code to SQL, or by executing user-defined functions in guest languages such as Python. In this work, we present a radically different approach that approximates an end-to-end inference pipeline (preprocessing plus prediction) using a light-weight embedding that discretizes a carefully selected subset of the input features and an index that maps data points in the embedding space to aggregated predictions of an ML model. We replace a complex preprocessing workflow and model-based inference with a simple feature transformation and an index lookup. Our framework improves inference latency by several orders of magnitude while maintaining similar prediction accuracy compared to the pipeline it approximates. 

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5. Collaborative Inference in Resource-Constrained Edge Networks: Challenges and Opportunities

   https://doi.org/10.1109/MILCOM61039.2024.10773876  

   Ng, Nathan; Souza, Abel; Diggavi, Suhas; Suri, Niranjan; Abdelzaher, Tarek; Towsley, Don; Shenoy, Prashant (October 2024, IEEE)

   Many IoT applications have increasingly adopted machine learning (ML) techniques, such as classification and detection, to enhance automation and decision-making processes. With advances in hardware accelerators such as Nvidia’s Jetson embedded GPUs, the computational capabilities of end devices, particularly for ML inference workloads, have significantly improved in recent years. These advances have opened opportunities for distributing computation across the edge network, enabling optimal resource utilization and reducing request latency. Previous research has demonstrated promising results in collaborative inference, where processing units in the edge network, such as end devices and edge servers, collaboratively execute an inference request to minimize latency.This paper explores approaches for implementing collaborative inference on a single model in resource-constrained edge networks, including on-device, device-edge, and edge-edge collaboration. We present preliminary results from proof-of-concept experiments to support each case. We discuss dynamic factors that can impact the performance of these inference execution strategies, such as network variability, thermal constraints, and workload fluctuations. Finally, we outline potential directions for future research. 

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