Level-4+ autonomous driving systems (ADS) must run dozens of heterogeneous deep neural networks (DNNs) as end-toend (E2E) pipelines under a strict latency constraint (≤100 ms), even as execution time varies by up to 3.3×. Cost rules out dedicating isolated hardware to each function in mass-produced ADS, so these DNNs must be densely colocated on a single chip, which introduces shared-resource contention. Tile-based accelerators expose two scheduling opportunities that conventional ADS schedulers do not exploit. First, they provide a tunable degree of parallelism (DoP): assigning more tiles raises DoP and can shorten DNN execution time. Second, they provide hardwarenative isolation: tiles can be physically partitioned among colocated DNNs. But using this flexibility is expensive: changing a task’s DoP triggers a stop-migrate-restart reallocation of its weights and intermediate features. At ADS task rates of 10–240 Hz, these stalls accumulate along E2E chains and threaten deadlines. Reservation-based schedulers fix DoP and leave this flexibility unused; work-conserving schedulers exploit it but assume reallocation is cheap and treat deadlines as independent. We present ADS-Tile, an isolation-aware scheduling framework that targets the reallocation cost of spatial DoP changes. ADS-Tile combines configurable isolation and elastic reservation into a spatio-temporal isolation-sharing space that bounds where and when reallocation occurs; a probabilistic latency model and a DAG-aware runtime scheduler then use this space to decide task colocation and DoP under shared E2E deadlines. On an industry- and academiaderived ADS benchmark, ADS-Tile uses up to 32% fewer tiles than the work-conserving baseline in deadline-critical settings and cuts reallocation-induced wasted processing capacity from 17%–44% to below 1.2%. Controlled spatio-temporal sharing improves resource efficiency and latency predictability for tile-based ADS.

Low-batch large language model (LLM) inference has been extensively applied to edge-side generative tasks, such as personal chat helper, virtual assistant, reception bot, private edge server, etc.To efficiently handle both prefill and decoding stages in LLM inference, near-memory processing (NMP) enabled heterogeneous computation paradigm has been proposed.However, existing NMP designs typically embed processing engines into DRAM dies, resulting in limited computation capacity, which in turn restricts their ability to accelerate edge-side low-batch LLM inference.To tackle this problem, we propose H 2 -LLM, a Hybrid-bondingbased Heterogeneous accelerator for edge-side low-batch LLM inference.To balance the trade-off between computation capacity and bandwidth intrinsic to hybrid-bonding technology, we propose * Co-corresponding authors.

Despite achieving impressive performance, current methods for detecting gaze targets, which depend on visual saliency and spatial scene geometry, continue to face challenges when it comes to detecting gaze targets within intricate image backgrounds. One of the primary reasons for this lies in the oversight of the intricate connection between human attention and activity cues. In this study, we introduce an innovative approach that amalgamates the visual saliency detection with the body-part & object interaction both guided by the soft gaze attention. This fusion enables precise and dependable detection of gaze targets amidst intricate image backgrounds. Our approach attains state-of-the-art performance on both the Gazefollow benchmark and the GazeVideoAttn benchmark. In comparison to recent methods that rely on intricate 3D reconstruction of a single input image, our approach, which solely leverages 2D image information, still exhibits a substantial lead across all evaluation metrics, positioning it closer to human-level performance. These outcomes underscore the potent effectiveness of our proposed method in the gaze target detection task.