Parallelizing CNN inference on heterogeneous edge clusters with data parallelism has gained popularity as a way to meet real-time requirements without sacrificing model accuracy. However, existing algorithms struggle to find optimal parallel granularity for complex CNNS, the structure of which is a directed acyclic graph (DAG) rather than a chain, and the parallel dimension is inflexible. To distribute the workload of modern CNNs on heterogeneous devices is also proven as NP-hard problem. In this article, we introduce DeepZoning , a versatile and cooperative inference framework that combines both model and data parallelism to accelerate CNN inference. DeepZoning employs two algorithms at different levels: (1) a low-level Adaptive Workload Partition algorithm that uses linear programming and takes spatial and channel dimensions into optimization during the search for feature map distribution on heterogeneous devices, and (2) a high-level Model Partition algorithm that finds the optimal model granularity and organizes complex CNNs into sequential zones to balance communication and computation during execution. Our experimental evaluations show that DeepZoning is effective, achieving up to a 3.02× speed improvement on our experimental prototype compared to state-of-the-art algorithms.

Recent progress in video anomaly detection suggests that the features of appearance and motion play crucial roles in distinguishing abnormal patterns from normal ones. However, we note that the effect of spatial scales of anomalies is ignored. The fact that many abnormal events occur in limited localized regions and severe background noise in-terferes with the learning of anomalous changes. Mean-while, most existing methods are limited by coarse-grained modeling approaches, which are inadequate for learning highly discriminative features to discriminate subtle differences between small-scale anomalies and normal patterns. To this end, this paper address multi-scale video anomaly detection by multi-grained spatiotemporal representation learning. We utilize video continuity to design three proxy tasks to perform feature learning at both coarse-grained and fine-grained levels, i.e., continuity judgment, discontinuity localization, and missing frame estimation. In particular, we formulate missing frame estimation as a contrastive learning task in feature space instead of a reconstruction task in RGB space to learn highly discriminative features. Experiments show that our proposed method outperforms state-of-the-art methods on four datasets, especially in scenes with small-scale anomalies.

Large Language Models (LLMs) has fostered the creation of innovative requirements. Locally deployed LLMs for micro-enterprise mitigates potential issues such as privacy infringements and sluggish response. However, they are hampered by the limitations in computing capability and memory space of possessed devices. We introduce PipeLLM, which allocates the model across devices commensurate with their computing capabilities. It enables the parallel execution of layers with slicing input sequence along the token dimension. PipeLLM demonstrates the potential to accelerate LLM inference with heterogeneity devices, offering a solution for LLM deployment in micro-enterprise hardware environment.

Partitioning a CNN and parallel executing inference with multiple IoT devices have gained popularity as a way to meet real-time requirements without sacrificing model accuracy. However, existing algorithms have struggled to find the optimal model partitioning granularity for complex CNNs. Additionally, executing inference with heterogeneous IoT devices is NP-hard when the structure of the CNN is a directed acyclic graph (DAG) rather than a chain. In this paper, we introduce a versatile and cooperative inference framework that combines both model and data parallelism to accelerate CNN inference. DeepZoning employs two algorithms at different levels: (1) a low-level Adaptive Workload Partition algorithm that uses linear programming and takes spatial and channel dimensions into optimization during the search for feature map distribution on heterogeneous devices, and (2) a high-level Model Partition algorithm that finds the optimal model granularity and organizes complex CNNs into sequential zones to balance communication and computation during execution.