Vision-Language Models (VLMs) demand substantial computational resources during inference, largely due to the extensive visual input tokens for representing visual information. Previous studies have noted that visual tokens tend to receive less attention than text tokens, suggesting their lower importance during inference and potential for pruning. However, their methods encounter several challenges: reliance on greedy heuristic criteria for token importance and incompatibility with FlashAttention and KV cache. To address these issues, we introduce TopV, a compatible TOken Pruning with inference Time Optimization for fast and low-memory VLM, achieving efficient pruning without additional training or fine-tuning. Instead of relying on attention scores, we formulate token pruning as an optimization problem, accurately identifying important visual tokens while remaining compatible with FlashAttention. Additionally, since we only perform this pruning once during the prefilling stage, it effectively reduces KV cache size. Our optimization framework incorporates a visual-aware cost function considering factors such as Feature Similarity, Relative Spatial Distance, and Absolute Central Distance, to measure the importance of each source visual token, enabling effective pruning of low-importance tokens. Extensive experiments demonstrate that our method outperforms previous token pruning methods, validating the effectiveness and efficiency of our approach.

Tiled architectures have emerged as a compelling platform for scaling deep neural network (DNN) execution, offering both compute density and communication efficiency.To harness their full potential, effective inter-layer scheduling is crucial for managing operation order, memory behavior, and compute resource coordination.However, current schedulers often fall short due to three persistent issues: incomplete treatment of core design factors, limited flexibility in handling diverse workload structures, and reliance on heuristic search algorithms with poor convergence.In this work, we trace these limitations to the absence of a unified and expressive scheduling representation.We introduce Crane, a framework that addresses these gaps through a hierarchical tableformat abstraction capable of encoding rich scheduling semantics.Crane supports both inference and training workloads, and reformulates scheduling as a mathematically structured optimization problem, enabling more complete and efficient exploration of the scheduling space.Evaluations show that Crane reduces energydelay product by up to 21.01× and improves scheduling speed by at least 2.82× over state-of-the-art baselines.

In this paper, we introduce a novel approach to enhancing neural network-based Simultaneous Localization and Mapping (SLAM) through the integration of model compression techniques and customized hardware architecture that focuses on micro-architectural and dataflow optimizations to improve computational efficiency and performance. Experiments across different scenarios demonstrate that the proposed approach achieves significant improvement.