Compared to Full-Model Fine-Tuning (FMFT), Parameter-Efficient Fine-Tuning (PEFT) has demonstrated superior efficacy and efficiency in several code understanding tasks, owing to PEFT’s ability to alleviate the catastrophic forgetting issue of Pre-trained Language Models (PLMs) by updating only a small number of parameters. However, existing studies primarily involve static code comprehension, aligning with the pre-training paradigm of recent PLMs and facilitating knowledge transfer, but they do not account for dynamic code changes. Thus, it remains unclear whether PEFT outperforms FMFT in task-specific adaptation for code-change-related tasks.To address this question, we examine four prevalent PEFT methods (i.e., AT, LoRA, PT, and PreT) and compare their performance with FMFT across seven popular PLMs. In experiments, two widely studied code-change-related tasks, i.e., Just-In-Time Defect Prediction (JIT-DP) and Commit Message Generation (CMG) are involved, demonstrating that the four PEFT methods can surpass FMFT on JIT-DP but only exhibit comparable performances at best on CMG in common scenarios. While in cross-lingual and low-resource scenarios, they exhibit relative superiority. Afterward, a series of probing tasks from both static and dynamic perspectives are conducted in this paper, offering detailed explanations for the efficacy of PEFT and FMFT. Inspired by the distinctive advantages of PEFT and FMFT in their layer-wise probing results, we propose Pasta$k$, a self-adaPtive efficient layer-specific tuning framework for PLMs in code change learning, which combines FMFT and PEFT during the domain adaptation according to the guidance of probing results. Experiments in the CMG task demonstrate that Pasta$k$surpasses diverse PEFT methods in effectiveness. Even, Pasta$k$outperforms FMFT by 1.48%, 3.21%, and 1.87% at most in terms of BLEU, Meteor, and Rouge-L, while saving 26.26% and 20.65% in terms of training time and computational memory compared with FMFT.

NV-FPGAs have attracted significant attention in research due to their high density, low leakage power, and reduced error rates. The nonvolatile memory (NVM) crossbar’s compute-in-memory (CiM) capability further enables NV-FPGAs to execute high-efficiency, high-throughput neural network (NN) inference tasks. However, with the rapid increase in network size and considering that the parameter size often exceeds the memory capacity of the field programmable gate array (FPGA), implementing the entire network on a single FPGA chip becomes impractical. In this article, we utilize FPGA’s inherent run time reprogramming feature to implement oversized NNs on NV-FPGAs. This approach splits NN models into multiple tasks for the cyclical execution. Specifically, we propose a performance-driven task adapter (PD-Adapter), which aims to achieve high-performance NN inference by employing the task deployment to optimize settings, such as processing element size and quantity, and the task switching to select the most suitable switching type for each task. We integrate the proposed PD-Adapter into an open-source toolchain and evaluate it. Experimental results demonstrate that the PD-Adapter can achieve a run time reduction of 85.37% and 76.12% compared to the baseline and execution-time-first policy, respectively.