The fast expansion of deep learning models has led to an increasing need for well-annotated datasets, while traditional manual annotation cannot meet this requirement. Current research on annotation mainly focuses on automating the annotation process. These studies typically rely on a set of predefined functionalities. However, in complex scenarios, for example, autonomous driving, annotation workflow, and postprocessing functions must be tailored to specific tasks. The challenge here lies in ensuring that newly generated functions integrate with the existing function set of the system, which requires the pipeline to understand the user requirement and real-time system context to generate appropriate input-output data structures. Previous annotation methods relied on human programming to meet this requirement. This dependency on professional assistance restricts the generalizability of annotation methods. Drawing on modern software engineering principles, we introduce an interactive code generation pipeline based on natural language input to address this challenge. Our approach supports real-time code generation by natural language input. To the best of our knowledge, this is one of the latest applications that apply customizable functional extensions in the annotation pipeline. Our evaluations on public datasets and a self-built real-world dataset demonstrate that our method significantly enhances the range of application scenarios for annotation tools while reducing manual intervention. Upon acceptance, the code will be open source.

Scheduling determines the execution order and time of operations in program. The order is related to operation dependencies, including data and resource dependencies. Data dependencies are intrinsic in programs, while resource dependencies are determined by scheduling methods. Existing scheduling methods lack an accurate and complete operation dependency graph (ODG), leading to poor performance. In this paper, we propose an ODG-based scheduling method for HLS with GNN and RL. We adopt GNN to perceive accurate relations between operations. We use the relations to guide an RL agent in building a complete ODG. We perform feedback-guided iterative scheduling with the graph to converge to a high-quality solution. Experiments show that our method reduces 23.8% and 16.4% latency on average, compared with the latest GNN-based and RL-based methods, respectively.

Scheduling determines the execution order and time of operations in a program. The order is related to operation dependencies, including data and resource dependencies. Data dependency is intrinsic in a program, showing operation data flow. Resource dependency is determined by scheduling methods, resolving operation resource contention. Existing scheduling methods focus on data dependency, rather than building and exploiting operation dependency graph (ODG) with extra resource dependency. As ODG contains all dependencies determining operation execution order, it provides global program information, facilitating efficient scheduling. In this work, we propose ODGS, a dependency-aware scheduling method for high-level synthesis with graph neural network (GNN) and reinforcement learning (RL). We adopt GNN to perceive accurate relations between operations. We use the relations to guide an RL agent in building a complete ODG. We perform feedback-guided iterative scheduling with ODG to converge to a high-quality solution. Experiments show that our method reduces 16.4% latency and 26.5% resource usage on average, compared with the latest RL-based method. Moreover, we reduce an average 2.9% latency over the GNN-based method under the same resource usage. The same resource usage is obtained by improving the GNN-based method with manual resource constraint tuning. Without tuning, its basic version consumes an average 237.6% more resources than our method.

Program behavior comprises operation dependency and resource requirement. They impact the performance of scheduling in high-level synthesis (HLS). Most existing scheduling methods focus on one aspect, resulting in poor performance. In this article, we propose PBS, a program behavior-aware scheduling method for HLS. We leverage a hybrid state encoding scheme to facilitate the comprehensive learning of program behaviors. Moreover, we propose bi-directional GNN and multiresolution aggregation schemes for learning complex operation dependency behavior. These schemes are integrated in an RL framework to iteratively improve scheduling solutions toward low latency and resource usage. Experiments show that PBS provides an average 32.7%, 26.3%, and 25.9% latency reductions, compared with the SDC, GNN-based, and RL-based methods, respectively.

As specified in Release 13 specification of the 3rd Generation Partnership Project (3GPP), narrowband physical uplink shared channel (NPUSCH) is a critical physical layer component of narrowband Internet-of-Things (NB-IoT) system. This paper designs and implements an efficient NPUSCH receiver by using modified discrete Fourier transform channel estimation and exponential moving average interpolation. Moreover, three key blocks including channel equalization, soft demodulation and soft combining are implemented for a full receive processing chain. Extensive simulation results corroborate that the designed receiver obtains lower block error rate than the benchmark specified by 3GPP.