# Review of Task-Scheduling Methods for Heterogeneous Chips
## 6 Discussion
### Future Direction  
The future development direction of the heterogeneous chip task-scheduling framework will combine more advanced technologies to improve the intelligence, flexibility, and reliability of the system. Among them, the distributed ledger technology based on blockchain provides new possibilities for building a decentralized collaborative mechanism. Through the immutability and distributed storage characteristics of blockchain, it can not only effectively solve the data consistency and trust problems in the task-scheduling process in a heterogeneous environment but also eliminate the dependence on central scheduling nodes in the traditional framework, thus significantly enhancing the fault tolerance and robustness [60] of the system. In addition, blockchain technology can record the whole process of task scheduling, realize the traceability of task allocation and resource use, and provide reliable data support for further optimization of scheduling methods. This innovative integration has laid a solid foundation for the continuous development of the heterogeneous chip task-scheduling framework and also provides more efficient and credible solutions for resource management and task allocation in complex computing environments. Moreover, the system model of the current scheduling framework has obvious deficiencies in handling the dynamic configuration of heterogeneous computingresources and real-time communication optimization, especially in its poor adaptability to the rapidly changing task demands and resource states. To solve this problem, future research can develop a dynamically adjusted system model based on graph theory [61] and network flow theory [62] and focus on improving the management efficiency of data flow and task scheduling. These models will optimize resource allocation and system response speed by implementing objective functions that minimize latency and maximize throughput. Furthermore, these models will support real-time monitoring and prediction capabilities that enable the system to respond quickly according to immediate changes in task load and resource status. In this way, it not only improves the adaptability of the system but also enhances its ability to handle complex computing tasks. The future development direction of the heterogeneous chip task-scheduling algorithm will move towards the direction of intelligence and adaptive optimization, among which deep integration with reinforcement learning is a key trend. Figure 9 describes the overall DeepSoCS network structure based on reinforcement learning. DeepSoCS simulates the interaction between the environment and the agent and constantly optimizes the scheduling method, which can dynamically adapt to the complex and variable resource states and task requirements in the heterogeneous chip system [63]. Compared with the traditional scheduling algorithm based on heuristic or static rules, the reinforcement learning method can perform online learning and real-time decision-making and can optimize strategies according to feedback in the operation, effectively improving the efficiency of task allocation and resource utilization. In addition, reinforcement learning algorithms can achieve global optimization in multi-task and multi-constraint heterogeneous environments through experience accumulation and policy migration, providing more flexible, efficient, and robust solutions for complex scheduling problems. However, heterogeneous chips have different processing units for computing power, memory bandwidth, power consumption, and other characteristics. Therefore, reinforcement learning algorithms must be able to choose appropriate task-scheduling strategies under these resource constraints. The state space needs to contain information such as load situation, data dependency, and task priority. In contrast, the action space includes the allocation mode of different tasks, such as the division and scheduling of tasks between different processing units.