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The continuous increase in computational power of GPUs, essential for advancements in areas like artificial intelligence and data processing, is driving the adoption of liquid cooling in data centers. Skived copper cold plates featuring…
Trapped ions are among the most promising platforms for realizing a large-scale quantum information processor. Current progress focuses on integrating optical and electronic components into microfabricated ion traps to allow scaling to…
The rising demand for generative large language models (LLMs) poses challenges for thermal and power management in cloud datacenters. Traditional techniques often are inadequate for LLM inference due to the fine-grained, millisecond-scale…
In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enlightened by prompt-tuning in Natural Language Processing (NLP), where learnable prompt tokens are…
A major computational bottleneck in modern High Energy Physics event generators arises from the integration of the matrix element, which requires repeated evaluations at different phase-space points to cover all possible initial- and…
Time-to-market pressure and productivity gap force vendors and researchers to improve embedded system design methodology. Current used design method, Register Transfer Level (RTL), is no longer be adequate to comply with embedded system…
This work introduces an electromagnetic metastructure based interconnect design that could address the critical need for electrical bandwidth and heat dissipation in high-speed, chiplet integration. We leverage silicon as the substrate for…
Thermal simulation plays a fundamental role in the thermal design of integrated circuits, especially 3D ICs. Current simulators require significant runtime for high-resolution simulation, and dismiss the complex nonlinear thermal effects,…
Modern deep neural networks must demonstrate state-of-the-art accuracy while exhibiting low latency and energy consumption. As such, neural architecture search (NAS) algorithms take these two constraints into account when generating a new…
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field…
2.5D chiplet systems have been proposed to improve the low manufacturing yield of large-scale chips. However, connecting the chiplets through an electronic interposer imposes a high traffic load on the interposer network. Silicon photonics…
Modern package designs make use of technologies such as backside power delivery (BSPD) and 3D stacked chiplets that require accounting for the heterogeneity in back end of the line (BEOL) structures in hot-spot prediction. Multiscale…
Multi-die FPGAs are crucial components in modern computing systems, particularly for high-performance applications such as artificial intelligence and data centers. Super long lines (SLLs) provide interconnections between super logic…
Edge computing facilitates deep learning in resource-constrained environments, but challenges such as resource heterogeneity and dynamic constraints persist. This paper introduces AMP4EC, an Adaptive Model Partitioning framework designed to…
As program workloads (e.g., AI) increase in size and algorithmic complexity, the primary challenge lies in their high dimensionality, encompassing computing cores, array sizes, and memory hierarchies. To overcome these obstacles, innovative…
As technology advances, photonic integrated circuits (PICs) are rapidly scaling in size and complexity, with modern designs integrating thousands of components. However, the analog custom layout nature of photonics, the curvy waveguide…
This paper investigates artificial intelligence (AI) methodologies for the synthesis and transpilation of permutation circuits across generic topologies. Our approach uses Reinforcement Learning (RL) techniques to achieve near-optimal…
Electrical Impedance Tomography (EIT) is a powerful tool for non-destructive evaluation, state estimation, and process tomography - among numerous other use cases. For these applications, and in order to reliably reconstruct images of a…
The application of Transformer-based large models has achieved numerous success in recent years. However, the exponential growth in the parameters of large models introduces formidable memory challenge for edge deployment. Prior works to…
In a typical RTL to GDSII flow, floorplanning or macro placement is a critical step in achieving decent quality of results (QoR). Moreover, in today's physical synthesis flows (e.g., Synopsys Fusion Compiler or Cadence Genus iSpatial), a…