Tight coupling between computation, communication, and control pervades the design and application of cyber-physical systems (CPSs). Due to the complexity of these systems, advanced design procedures that account for these tight interconnections are paramount to ensure the safe and reliable operation of control algorithms under computational constraints. This paper presents the Simulator for Hardware Architecture and Real-time Control (Sharc) to assist in the co-design of control algorithms and the computational hardware on which they are run. Sharc simulates the execution of a user-specified control algorithm on a given processor microarchitecture configuration, evaluating how computational constraints affect the dynamical properties of the closed-loop system. We illustrate the power of Sharc by examples of MPC applied to adaptive cruise control and the stabilization of an inverted pendulum. Sharc can be found at github.com/pwintz/sharc.

Datacenter applications exhibit large instruction footprints causing significant instruction cache misses and, as a result, frontend stalls. To address this issue, instruction prefetching mechanisms have been proposed, including state-of-the-art techniques such as fetch-directed instruction prefetching. However, our study shows that existing implementations still fall far short of an ideal system with a perfect instruction cache. In particular, up to $588.47 \%$ of potential IPC speedup of existing processors hides due to frontend stalls, and these frontend stalls are due to inaccurate and untimely instruction prefetches. We quantify the impact of these individual effects, observing that applications exhibit different characteristics that call for adaptive application-specific optimizations. Based on these insights, we propose two novel mechanisms, UDP and UFTQ, to improve the accuracy of FDIP without negatively affecting timeliness while leveraging prefetches on the wrong path. We evaluate our technique on 10 data center workloads showing a maximal IPC improvement of $16.1 \%$ and an average IPC improvement of $3.6 \%$. Our techniques only introduce moderate hardware modifications and a storage cost of 8 KB.

Abstract Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

Advancements in prompt tuning of vision-language models have underscored their potential in enhancing open-world visual concept comprehension. However, prior works only primarily focus on single-mode (only one prompt for each modality) and holistic level (image or sentence) semantic alignment, which fails to capture the sample diversity, leading to sub-optimal prompt discovery. To address the limitation, we propose a multi-mode token-level tuning framework that leverages the optimal transportation to learn and align a set of prompt tokens across modalities. Specifically, we rely on two essential factors: 1) multi-mode prompts discovery, which guarantees diverse semantic representations, and 2) token-level alignment, which helps explore fine-grained similarity. Consequently, the similarity can be calculated as a hierarchical transportation problem between the modality-specific sets. Extensive experiments on popular image recognition benchmarks show the superior generalization and few-shot abilities of our approach. The qualitative analysis demonstrates that the learned prompt tokens have the ability to capture diverse visual concepts.