pwc-archive/papers-with-abstracts huggingface dataset:
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures
Authors: Marco Siracusa, Olivia Hsu, Victor Soria-Pardos, Joshua Randall, Arnaud Grasset, Eric Biscondi, Doug Joseph, Randy Allen, Fredrik Kjolstad, Miquel Moretó Planas, Adrià Armejach
Date: 2025-04-14 00:00:00
Tasks: Graph Learning

Irregular embedding lookups are a critical bottleneck in recommender models, sparse large language models, and graph learning models. In this paper, we first demonstrate that, by offloading these lookups to specialized access units, Decoupled Access-Execute (DAE) processors achieve 2.6$\times$ higher performance and 6.4$\times$ higher performance/watt than GPUs on end-to-end models. Then, we propose the Ember compiler for automatically generating optimized DAE code from PyTorch and TensorFlow. Conversely from other DAE compilers, Ember features multiple intermediate representations specifically designed for different optimization levels. In this way, Ember can implement all optimizations to match the performance of hand-written code, unlocking the full potential of DAE architectures at scale.
Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability
Authors: Aikaterini Maria Panteleaki, Konstantinos Balaskas, Georgios Zervakis, Hussam Amrouch, Iraklis Anagnostopoulos
Date: 2025-04-14 00:00:00

As Deep Neural Networks (DNNs) continue to drive advancements in artificial intelligence, the design of hardware accelerators faces growing concerns over embodied carbon footprint due to complex fabrication processes. 3D integration improves performance but introduces sustainability challenges, making carbon-aware optimization essential. In this work, we propose a carbon-efficient design methodology for 3D DNN accelerators, leveraging approximate computing and genetic algorithm-based design space exploration to optimize Carbon Delay Product (CDP). By integrating area-efficient approximate multipliers into Multiply-Accumulate (MAC) units, our approach effectively reduces silicon area and fabrication overhead while maintaining high computational accuracy. Experimental evaluations across three technology nodes (45nm, 14nm, and 7nm) show that our method reduces embodied carbon by up to 30% with negligible accuracy drop.
Understanding and Optimizing Multi-Stage AI Inference Pipelines
Authors: Abhimanyu Rajeshkumar Bambhaniya, Hanjiang Wu, Suvinay Subramanian, Sudarshan Srinivasan, Souvik Kundu, Amir Yazdanbakhsh, Midhilesh Elavazhagan, Madhu Kumar, Tushar Krishna
Date: 2025-04-14 00:00:00
Tasks: CPU, Navigate, RAG, Retrieval, Retrieval-augmented Generation

The rapid evolution of Large Language Models (LLMs) has driven the need for increasingly sophisticated inference pipelines and hardware platforms. Modern LLM serving extends beyond traditional prefill-decode workflows, incorporating multi-stage processes such as Retrieval Augmented Generation (RAG), key-value (KV) cache retrieval, dynamic model routing, and multi step reasoning. These stages exhibit diverse computational demands, requiring distributed systems that integrate GPUs, ASICs, CPUs, and memory-centric architectures. However, existing simulators lack the fidelity to model these heterogeneous, multi-engine workflows, limiting their ability to inform architectural decisions. To address this gap, we introduce HERMES, a Heterogeneous Multi-stage LLM inference Execution Simulator. HERMES models diverse request stages; including RAG, KV retrieval, reasoning, prefill, and decode across complex hardware hierarchies. HERMES supports heterogeneous clients executing multiple models concurrently unlike prior frameworks while incorporating advanced batching strategies and multi-level memory hierarchies. By integrating real hardware traces with analytical modeling, HERMES captures critical trade-offs such as memory bandwidth contention, inter-cluster communication latency, and batching efficiency in hybrid CPU-accelerator deployments. Through case studies, we explore the impact of reasoning stages on end-to-end latency, optimal batching strategies for hybrid pipelines, and the architectural implications of remote KV cache retrieval. HERMES empowers system designers to navigate the evolving landscape of LLM inference, providing actionable insights into optimizing hardware-software co-design for next-generation AI workloads.
GenEDA: Unleashing Generative Reasoning on Netlist via Multimodal Encoder-Decoder Aligned Foundation Model
Authors: Wenji Fang, Jing Wang, Yao Lu, Shang Liu, Zhiyao Xie
Date: 2025-04-13 00:00:00
Tasks: Decoder, Type prediction

The success of foundation AI has motivated the research of circuit foundation models, which are customized to assist the integrated circuit (IC) design process. However, existing pre-trained circuit models are typically limited to standalone encoders for predictive tasks or decoders for generative tasks. These two model types are developed independently, operate on different circuit modalities, and reside in separate latent spaces, which restricts their ability to complement each other for more advanced applications. In this work, we present GenEDA, the first framework that aligns circuit encoders with decoders within a shared latent space. GenEDA bridges the gap between graph-based circuit representations and text-based large language models (LLMs), enabling communication between their respective latent spaces. To achieve the alignment, we propose two paradigms that support both open-source trainable LLMs and commercial frozen LLMs. Built on this aligned architecture, GenEDA enables three unprecedented generative reasoning tasks over netlists, where the model reversely generates the high-level functionality from low-level netlists in different granularities. These tasks extend traditional gate-type prediction to direct generation of full-circuit functionality. Experiments demonstrate that GenEDA significantly boosts advanced LLMs' (e.g., GPT-4o and DeepSeek-V3) performance in all tasks.
NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed Graph
Authors: Wenji Fang, Wenkai Li, Shang Liu, Yao Lu, Hongce Zhang, Zhiyao Xie
Date: 2025-04-12 00:00:00
Tasks: Graph Learning, Representation Learning

Circuit representation learning has shown promise in advancing Electronic Design Automation (EDA) by capturing structural and functional circuit properties for various tasks. Existing pre-trained solutions rely on graph learning with complex functional supervision, such as truth table simulation. However, they only handle simple and-inverter graphs (AIGs), struggling to fully encode other complex gate functionalities. While large language models (LLMs) excel at functional understanding, they lack the structural awareness for flattened netlists. To advance netlist representation learning, we present NetTAG, a netlist foundation model that fuses gate semantics with graph structure, handling diverse gate types and supporting a variety of functional and physical tasks. Moving beyond existing graph-only methods, NetTAG formulates netlists as text-attributed graphs, with gates annotated by symbolic logic expressions and physical characteristics as text attributes. Its multimodal architecture combines an LLM-based text encoder for gate semantics and a graph transformer for global structure. Pre-trained with gate and graph self-supervised objectives and aligned with RTL and layout stages, NetTAG captures comprehensive circuit intrinsics. Experimental results show that NetTAG consistently outperforms each task-specific method on four largely different functional and physical tasks and surpasses state-of-the-art AIG encoders, demonstrating its versatility.
MGS: Markov Greedy Sums for Accurate Low-Bitwidth Floating-Point Accumulation
Authors: Vikas Natesh, H. T. Kung, David Kong
Date: 2025-04-12 00:00:00
Tasks: image-classification, Image Classification

We offer a novel approach, MGS (Markov Greedy Sums), to improve the accuracy of low-bitwidth floating-point dot products in neural network computations. In conventional 32-bit floating-point summation, adding values with different exponents may lead to loss of precision in the mantissa of the smaller term, which is right-shifted to align with the larger term's exponent. Such shifting (a.k.a. 'swamping') is a significant source of numerical errors in accumulation when implementing low-bitwidth dot products (e.g., 8-bit floating point) as the mantissa has a small number of bits. We avoid most swamping errors by arranging the terms in dot product summation based on their exponents and summing the mantissas without overflowing the low-bitwidth accumulator. We design, analyze, and implement the algorithm to minimize 8-bit floating point error at inference time for several neural networks. In contrast to traditional sequential summation, our method has significantly lowered numerical errors, achieving classification accuracy on par with high-precision floating-point baselines for multiple image classification tasks. Our dMAC hardware units can reduce power consumption by up to 34.1\% relative to conventional MAC units.
ML For Hardware Design Interpretability: Challenges and Opportunities
Authors: Raymond Baartmans, Andrew Ensinger, Victor Agostinelli, Lizhong Chen
Date: 2025-04-11 00:00:00

The increasing size and complexity of machine learning (ML) models have driven the growing need for custom hardware accelerators capable of efficiently supporting ML workloads. However, the design of such accelerators remains a time-consuming process, heavily relying on engineers to manually ensure design interpretability through clear documentation and effective communication. Recent advances in large language models (LLMs) offer a promising opportunity to automate these design interpretability tasks, particularly the generation of natural language descriptions for register-transfer level (RTL) code, what we refer to as "RTL-to-NL tasks." In this paper, we examine how design interpretability, particularly in RTL-to-NL tasks, influences the efficiency of the hardware design process. We review existing work adapting LLMs for these tasks, highlight key challenges that remain unaddressed, including those related to data, computation, and model development, and identify opportunities to address them. By doing so, we aim to guide future research in leveraging ML to automate RTL-to-NL tasks and improve hardware design interpretability, thereby accelerating the hardware design process and meeting the increasing demand for custom hardware accelerators in machine learning and beyond.
BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers
Authors: Rui Zhang, Liang Li, Stéphane Lanteri, Hao Kang, Jiaqi Li
Date: 2025-04-14 00:00:00
Tasks: Bayesian Optimization

Physics-informed neural networks (PINNs) is becoming a popular alternative method for solving partial differential equations (PDEs). However, they require dedicated manual modifications to the hyperparameters of the network, the sampling methods and loss function weights for different PDEs, which reduces the efficiency of the solvers. In this paper, we pro- pose a general multi-stage framework, i.e. BO-SA-PINNs to alleviate this issue. In the first stage, Bayesian optimization (BO) is used to select hyperparameters for the training process, and based on the results of the pre-training, the network architecture, learning rate, sampling points distribution and loss function weights suitable for the PDEs are automatically determined. The proposed hyperparameters search space based on experimental results can enhance the efficiency of BO in identifying optimal hyperparameters. After selecting the appropriate hyperparameters, we incorporate a global self-adaptive (SA) mechanism the second stage. Using the pre-trained model and loss information in the second-stage training, the exponential moving average (EMA) method is employed to optimize the loss function weights, and residual-based adaptive refinement with distribution (RAR-D) is used to optimize the sampling points distribution. In the third stage, L-BFGS is used for stable training. In addition, we introduce a new activation function that enables BO-SA-PINNs to achieve higher accuracy. In numerical experiments, we conduct comparative and ablation experiments to verify the performance of the model on Helmholtz, Maxwell, Burgers and high-dimensional Poisson equations. The comparative experiment results show that our model can achieve higher accuracy and fewer iterations in test cases, and the ablation experiments demonstrate the positive impact of every improvement.
Causal integration of chemical structures improves representations of microscopy images for morphological profiling
Authors: Yemin Yu, Neil Tenenholtz, Lester Mackey, Ying WEI, David Alvarez-Melis, Ava P. Amini, Alex X. Lu
Date: 2025-04-13 00:00:00
Tasks: Contrastive Learning, counterfactual, Representation Learning

Recent advances in self-supervised deep learning have improved our ability to quantify cellular morphological changes in high-throughput microscopy screens, a process known as morphological profiling. However, most current methods only learn from images, despite many screens being inherently multimodal, as they involve both a chemical or genetic perturbation as well as an image-based readout. We hypothesized that incorporating chemical compound structure during self-supervised pre-training could improve learned representations of images in high-throughput microscopy screens. We introduce a representation learning framework, MICON (Molecular-Image Contrastive Learning), that models chemical compounds as treatments that induce counterfactual transformations of cell phenotypes. MICON significantly outperforms classical hand-crafted features such as CellProfiler and existing deep-learning-based representation learning methods in challenging evaluation settings where models must identify reproducible effects of drugs across independent replicates and data-generating centers. We demonstrate that incorporating chemical compound information into the learning process provides consistent improvements in our evaluation setting and that modeling compounds specifically as treatments in a causal framework outperforms approaches that directly align images and compounds in a single representation space. Our findings point to a new direction for representation learning in morphological profiling, suggesting that methods should explicitly account for the multimodal nature of microscopy screening data.
Exponential Shift: Humans Adapt to AI Economies
Authors: Kevin J McNamara, Rhea Pritham Marpu
Date: 2025-04-11 00:00:00
Tasks: Emotional Intelligence

This paper explores how artificial intelligence (AI) and robotics are transforming the global labor market. Human workers, limited to a 33% duty cycle due to rest and holidays, cost $14 to $55 per hour. In contrast, digital labor operates nearly 24/7 at just $0.10 to $0.50 per hour. We examine sectors like healthcare, education, manufacturing, and retail, finding that 40-70% of tasks could be automated. Yet, human skills like emotional intelligence and adaptability remain essential. Humans process 5,000-20,000 tokens (units of information) per hour, while AI far exceeds this, though its energy use-3.5 to 7 times higher than humans-could offset 20-40% of cost savings. Using real-world examples, such as AI in journalism and law, we illustrate these dynamics and propose six strategies-like a 4-day workweek and retraining-to ensure a fair transition to an AI-driven economy.
Exploring Cognitive Attributes in Financial Decision-Making
Authors: Mallika Mainali, Rosina O. Weber
Date: 2025-04-11 00:00:00
Tasks: Decision Making

Cognitive attributes are fundamental to metacognition, shaping how individuals process information, evaluate choices, and make decisions. To develop metacognitive artificial intelligence (AI) models that reflect human reasoning, it is essential to account for the attributes that influence reasoning patterns and decision-maker behavior, often leading to different or even conflicting choices. This makes it crucial to incorporate cognitive attributes in designing AI models that align with human decision-making processes, especially in high-stakes domains such as finance, where decisions have significant real-world consequences. However, existing AI alignment research has primarily focused on value alignment, often overlooking the role of individual cognitive attributes that distinguish decision-makers. To address this issue, this paper (1) analyzes the literature on cognitive attributes, (2) establishes five criteria for defining them, and (3) categorizes 19 domain-specific cognitive attributes relevant to financial decision-making. These three components provide a strong basis for developing AI systems that accurately reflect and align with human decision-making processes in financial contexts.
Artificial Intelligence and the Dual Paradoxes: Examining the Interplay of Efficiency, Resource Consumption, and Labor Dynamics
Authors: Mfon Akpan, Adeyemi Adebayo
Date: 2025-04-09 00:00:00
Tasks: Decision Making, Ethics

Artificial Intelligence's (AI) rapid development and growth not only transformed industries but also fired up important debates about its impacts on employment, resource allocation, and the ethics involved in decision-making. It serves to understand how changes within an industry will be able to influence society with that change. Advancing AI technologies will create a dual paradox of efficiency, greater resource consumption, and displacement of traditional labor. In this context, we explore the impact of AI on energy consumption, human labor roles, and hybrid roles widespread human labor replacement. We used mixed methods involving qualitative and quantitative analyses of data identified from various sources. Findings suggest that AI increases energy consumption and has impacted human labor roles to a minimal extent, considering that its applicability is limited to some tasks that require human judgment. In this context, the
Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization
Authors: Zirui Chen, Zhaoyang Zhang, Ziqing Xing, Ridong Li, Zhaohui Yang, Richeng Jin, Chongwen Huang, Yuzhi Yang, Mérouane Debbah
Date: 2025-04-09 00:00:00

Existing learning models often exhibit poor generalization when deployed across diverse scenarios. It is mainly due to that the underlying reference frame of the data varies with the deployment environment and settings. However, despite the data of each scenario has its distinct reference frame, its generation generally follows the same underlying physical rule. Based on these findings, this article proposes a brand-new universal deep learning framework named analogical learning (AL), which provides a highly efficient way to implicitly retrieve the reference frame information associated with a scenario and then to make accurate prediction by relative analogy across scenarios. Specifically, an elegant bipartite neural network architecture called Mateformer is designed, the first part of which calculates the relativity within multiple feature spaces between the input data and a small amount of embedded data from the current scenario, while the second part uses these relativity to guide the nonlinear analogy. We apply AL to the typical multi-scenario learning problem of intelligent wireless localization in cellular networks. Extensive experiments show that AL achieves state-of-the-art accuracy, stable transferability and robust adaptation to new scenarios without any tuning, and outperforming conventional methods with a precision improvement of nearly two orders of magnitude. All data and code are available at https://github.com/ziruichen-research/ALLoc.
Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance
Authors: Peter Matthew Jacobs, Foad Namjoo, Jeff M. Phillips
Date: 2025-04-15 00:00:00

We revisit extending the Kolmogorov-Smirnov distance between probability distributions to the multidimensional setting and make new arguments about the proper way to approach this generalization. Our proposed formulation maximizes the difference over orthogonal dominating rectangular ranges (d-sided rectangles in R^d), and is an integral probability metric. We also prove that the distance between a distribution and a sample from the distribution converges to 0 as the sample size grows, and bound this rate. Moreover, we show that one can, up to this same approximation error, compute the distance efficiently in 4 or fewer dimensions; specifically the runtime is near-linear in the size of the sample needed for that error. With this, we derive a delta-precision two-sample hypothesis test using this distance. Finally, we show these metric and approximation properties do not hold for other popular variants.
Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE) of 2D Black Box Classifier Functions
Authors: Mithun Goutham, Riccardo DalferroNucci, Stephanie Stockar, Meghna Menon, Sneha Nayak, Harshad Zade, Chetan Patel, Mario Santillo
Date: 2025-04-13 00:00:00

Accurately estimating decision boundaries in black box systems is critical when ensuring safety, quality, and feasibility in real-world applications. However, existing methods iteratively refine boundary estimates by sampling in regions of uncertainty, without providing guarantees on the closeness to the decision boundary and also result in unnecessary exploration that is especially disadvantageous when evaluations are costly. This paper presents the Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE), a sample efficient and function-agnostic algorithm that leverages the intermediate value theorem to estimate the location of the decision boundary of a black box binary classifier within a user-specified epsilon-neighborhood. Evaluations are conducted on three nonlinear test functions and a case study of an electric grid stability problem with uncertain renewable power injection. The EDGE algorithm demonstrates superior sample efficiency and better boundary approximation than adaptive sampling techniques and grid-based searches.
MASH: Masked Anchored SpHerical Distances for 3D Shape Representation and Generation
Authors: Changhao Li, Yu Xin, Xiaowei Zhou, Ariel Shamir, Hao Zhang, Ligang Liu, Ruizhen Hu
Date: 2025-04-12 00:00:00
Tasks: 3D Shape Representation, Surface Reconstruction

We introduce Masked Anchored SpHerical Distances (MASH), a novel multi-view and parametrized representation of 3D shapes. Inspired by multi-view geometry and motivated by the importance of perceptual shape understanding for learning 3D shapes, MASH represents a 3D shape as a collection of observable local surface patches, each defined by a spherical distance function emanating from an anchor point. We further leverage the compactness of spherical harmonics to encode the MASH functions, combined with a generalized view cone with a parameterized base that masks the spatial extent of the spherical function to attain locality. We develop a differentiable optimization algorithm capable of converting any point cloud into a MASH representation accurately approximating ground-truth surfaces with arbitrary geometry and topology. Extensive experiments demonstrate that MASH is versatile for multiple applications including surface reconstruction, shape generation, completion, and blending, achieving superior performance thanks to its unique representation encompassing both implicit and explicit features.