Integrating domain-specific operators into processor cores is essential for performance scaling. However, multi-operand operators often face a semantic gap with conventional ISAs, which are limited in operand capacity and scheduling flexibility. This paper presents SMIX, a schedulable instruction set extension interface for multi-operand operators. SMIX decouples execution into three stages: out-of-order input filling, computation, and out-of-order result picking. By employing explicit encoding and counter-based dependency management, SMIX enables both efficient static scheduling by compilers and dynamic out-of-order execution in hardware. Experimental results demonstrate the high schedulability of SMIX, where static scheduling provides an average 12% performance gain on the Rocket core and dynamic out-of-order scheduling contributes an additional 9.2% speedup on the BOOM core, all while maintaining minimal hardware overhead.

With the development of human-machine dialogue technology, more and more companies have launched their cognitive service products, such as Virtual Personal Assistant (VPA), smart speakers, shopping guide robots, etc. However, in these practical applications, most of the bots passively respond to user's utterances, lacking user preference knowledge and the proactive consciousness to lead the dialogue. Therefore, it is essential that bots proactively and naturally lead the dialogue from chitchat to service recommendation to meet user's requirements. To address this challenge, bots not only needs to detect the user's dialogue goal in real time, but also needs to plan a goal sequence based on user profile. In this paper, we propose DGPF, a Dialogue Goal Planning Framework. DGPF plans a reasonable goal sequence grounded on user's interests and personal KB before the conversation, additionally predicts user's true intent (i.e. dialogue goal) and judges whether the goal is completed based on the utterances during the conversation. DGPF includes a novel joint learning model that can simultaneously fix the two sub-tasks of goal completion estimation as well as current goal prediction, and improve each other's performance interactively. Our experimental results on the open dataset DuRecDial have been significantly improved compared to the baseline, which proves the effectiveness of our framework.