pwc-archive/papers-with-abstracts huggingface dataset:
CORE: Constraint-Aware One-Step Reinforcement Learning for Simulation-Guided Neural Network Accelerator Design
Authors: Yifeng Xiao, Yurong Xu, Ning Yan, Masood Mortazavi, Pierluigi Nuzzo
Date: 2025-06-04 00:00:00
Tasks: Reinforcement Learning (RL)

Simulation-based design space exploration (DSE) aims to efficiently optimize high-dimensional structured designs under complex constraints and expensive evaluation costs. Existing approaches, including heuristic and multi-step reinforcement learning (RL) methods, struggle to balance sampling efficiency and constraint satisfaction due to sparse, delayed feedback, and large hybrid action spaces. In this paper, we introduce CORE, a constraint-aware, one-step RL method for simulationguided DSE. In CORE, the policy agent learns to sample design configurations by defining a structured distribution over them, incorporating dependencies via a scaling-graph-based decoder, and by reward shaping to penalize invalid designs based on the feedback obtained from simulation. CORE updates the policy using a surrogate objective that compares the rewards of designs within a sampled batch, without learning a value function. This critic-free formulation enables efficient learning by encouraging the selection of higher-reward designs. We instantiate CORE for hardware-mapping co-design of neural network accelerators, demonstrating that it significantly improves sample efficiency and achieves better accelerator configurations compared to state-of-the-art baselines. Our approach is general and applicable to a broad class of discrete-continuous constrained design problems.
Minimal Neuron Circuits -- Part I: Resonators
Authors: Amr Nabil, T. Nandha Kumar, Haider Abbas F. Almurib
Date: 2025-06-03 00:00:00

Spiking Neural Networks have earned increased recognition in recent years owing to their biological plausibility and event-driven computation. Spiking neurons are the fundamental building components of Spiking Neural Networks. Those neurons act as computational units that determine the decision to fire an action potential. This work presents a methodology to implement biologically plausible yet scalable spiking neurons in hardware. We show that it is more efficient to design neurons that mimic the $I_{Na,p}+I_{K}$ model rather than the more complicated Hodgkin-Huxley model. We demonstrate our methodology by presenting eleven novel minimal spiking neuron circuits in Parts I and II of the paper. We categorize the neuron circuits presented into two types: Resonators and Integrators. We discuss the methodology employed in designing neurons of the resonator type in Part I, while we discuss neurons of the integrator type in Part II. In part I, we postulate that Sodium channels exhibit type-N negative differential resistance. Consequently, we present three novel minimal neuron circuits that use type-N negative differential resistance circuits or devices as the Sodium channel. Nevertheless, the aim of the paper is not to present a set of minimal neuron circuits but rather the methodology utilized to construct those circuits.
Memory Access Characterization of Large Language Models in CPU Environment and its Potential Impacts
Authors: Spencer Banasik
Date: 2025-06-02 00:00:00
Tasks: CPU

As machine learning algorithms are shown to be an increasingly valuable tool, the demand for their access has grown accordingly. Oftentimes, it is infeasible to run inference with larger models without an accelerator, which may be unavailable in environments that have constraints such as energy consumption, security, or cost. To increase the availability of these models, we aim to improve the LLM inference speed on a CPU-only environment by modifying the cache architecture. To determine what improvements could be made, we conducted two experiments using Llama.cpp and the QWEN model: running various cache configurations and evaluating their performance, and outputting a trace of the memory footprint. Using these experiments, we investigate the memory access patterns and performance characteristics to identify potential optimizations.
FlexiSAGA: A Flexible Systolic Array GEMM Accelerator for Sparse and Dense Processing
Authors: Mika Markus Müller, Konstantin Lübeck, Alexander Louis-Ferdinand Jung, Jannik Steinmetz, Oliver Bringmann
Date: 2025-06-02 00:00:00

Artificial Intelligence (AI) algorithms, such as Deep Neural Networks (DNNs), have become an important tool for a wide range of applications, from computer vision to natural language processing. However, the computational complexity of DNN inference poses a significant challenge, particularly for processing on resource-constrained edge devices. One promising approach to address this challenge is the exploitation of sparsity in DNN operator weights. In this work, we present FlexiSAGA, an architecturally configurable and dataflow-flexible AI hardware accelerator for the sparse and dense processing of general matrix multiplications (GEMMs). FlexiSAGA supports seven different sparse and dense dataflows, enabling efficient processing of resource intensive DNN operators. Additionally, we propose a DNN pruning method specifically tailored towards the FlexiSAGA architecture, allowing for near-optimal processing of dense and sparse convolution and fully-connected operators, facilitating a DNN/HW co-design flow. Our results show a whole DNN sparse-over-dense inference speedup ranging from 1.41 up to 4.28, outperforming commercial and literature-reported accelerator platforms.
SpiceMixer -- Netlist-Level Circuit Evolution
Authors: Stefan Uhlich, Andrea Bonetti, Arun Venkitaraman, Chia-Yu Hsieh, Mustafa Emre Gürsoy, Ryoga Matsuo, Lorenzo Servadei
Date: 2025-06-02 00:00:00

This paper introduces SpiceMixer, a genetic algorithm developed to synthesize novel analog circuits by evolving SPICE netlists. Unlike conventional methods, SpiceMixer operates directly on netlist lines, enabling compatibility with any component or subcircuit type and supporting general-purpose genetic operations. By using a normalized netlist format, the algorithm enhances the effectiveness of its genetic operators: crossover, mutation, and pruning. We show that SpiceMixer achieves superior performance in synthesizing standard cells (inverter, two-input NAND, and latch) and in designing an analog classifier circuit for the Iris dataset, reaching an accuracy of 89% on the test set. Across all evaluated tasks, SpiceMixer consistently outperforms existing synthesis methods.
VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration
Authors: Shereef Helal, Alberto Garcia-Ortiz, Lennart Bamberg
Date: 2025-06-01 00:00:00

Leveraging high degrees of unstructured sparsity is a promising approach to enhance the efficiency of deep neural network DNN accelerators - particularly important for emerging Edge-AI applications. We introduce VUSA, a systolic-array architecture that virtually grows based on the present sparsity to perform larger matrix multiplications with the same number of physical multiply-accumulate MAC units. The proposed architecture achieves saving by 37% and 68% in area and power efficiency, respectively, at the same peak-performance, compared to a baseline systolic array architecture in a commercial 16-nm technology. Still, the proposed architecture supports acceleration for any DNN with any sparsity - even no sparsity at all. Thus, the proposed architecture is application-independent, making it viable for general-purpose AI acceleration.
Processing-in-memory for genomics workloads
Authors: William Andrew Simon, Leonid Yavits, Konstantina Koliogeorgi, Yann Falevoz, Yoshihiro Shibuya, Dominique Lavenier, Irem Boybat, Klea Zambaku, Berkan Şahin, Mohammad Sadrosadati, Onur Mutlu, Abu Sebastian, Rayan Chikhi, The BioPIM Consortium, Can Alkan
Date: 2025-05-31 00:00:00

Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the main workforce for the life sciences. Genome sequencing is now becoming a part of Predictive, Preventive, Personalized, and Participatory (termed 'P4') medicine. All genomic data are currently processed in energy-hungry computer clusters and centers, necessitating data transfer, consuming substantial energy, and wasting valuable time. Therefore, there is a need for fast, energy-efficient, and cost-efficient technologies that enable genomics research without requiring data centers and cloud platforms. We recently started the BioPIM Project to leverage the emerging processing-in-memory (PIM) technologies to enable energy and cost-efficient analysis of bioinformatics workloads. The BioPIM Project focuses on co-designing algorithms and data structures commonly used in genomics with several PIM architectures for the highest cost, energy, and time savings benefit.
PointODE: Lightweight Point Cloud Learning with Neural Ordinary Differential Equations on Edge
Authors: Keisuke Sugiura, Mizuki Yasuda, Hiroki Matsutani
Date: 2025-05-31 00:00:00
Tasks: CPU

Embedded edge devices are often used as a computing platform to run real-world point cloud applications, but recent deep learning-based methods may not fit on such devices due to limited resources. In this paper, we aim to fill this gap by introducing PointODE, a parameter-efficient ResNet-like architecture for point cloud feature extraction based on a stack of MLP blocks with residual connections. We leverage Neural ODE (Ordinary Differential Equation), a continuous-depth version of ResNet originally developed for modeling the dynamics of continuous-time systems, to compress PointODE by reusing the same parameters across MLP blocks. The point-wise normalization is proposed for PointODE to handle the non-uniform distribution of feature points. We introduce PointODE-Elite as a lightweight version with 0.58M trainable parameters and design its dedicated accelerator for embedded FPGAs. The accelerator consists of a four-stage pipeline to parallelize the feature extraction for multiple points and stores the entire parameters on-chip to eliminate most of the off-chip data transfers. Compared to the ARM Cortex-A53 CPU, the accelerator implemented on a Xilinx ZCU104 board speeds up the feature extraction by 4.9x, leading to 3.7x faster inference and 3.5x better energy-efficiency. Despite the simple architecture, PointODE-Elite shows competitive accuracy to the state-of-the-art models on both synthetic and real-world classification datasets, greatly improving the trade-off between accuracy and inference cost.
COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
Authors: Chamika Sudusinghe, Gerasimos Gerogiannis, Damitha Lenadora, Charles Block, Josep Torrellas, Charith Mendis
Date: 2025-05-31 00:00:00
Tasks: Transfer Learning

Sparse tensor programs are essential in deep learning and graph analytics, driving the need for optimized processing. To meet this demand, specialized hardware accelerators are being developed. Optimizing these programs for accelerators is challenging for two reasons: program performance is highly sensitive to variations in sparse inputs, and early-stage accelerators rely on expensive simulators. Therefore, ML-based cost models used for optimizing such programs on general-purpose hardware are often ineffective for early-stage accelerators, as they require large datasets for proper training. To this end, we introduce COGNATE, a novel framework that leverages inexpensive data samples from general-purpose hardware (e.g., CPUs) to train cost models, followed by few-shot fine-tuning on emerging hardware. COGNATE exploits the homogeneity of input features across hardware platforms while effectively mitigating heterogeneity, enabling cost model training with just 5% of the data samples needed by accelerator-specific models to achieve comparable performance. We conduct extensive experiments to demonstrate that COGNATE outperforms existing techniques, achieving average speedups of 1.47x (up to 5.46x) for SpMM and 1.39x (up to 4.22x) for SDDMM.
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study
Authors: Yaşar Utku Alçalar, Yu Cao, Mehmet Akçakaya
Date: 2025-05-30 00:00:00
Tasks: Computational Efficiency, Edge-computing, MRI Reconstruction, Quantization

Physics-driven artificial intelligence (PD-AI) reconstruction methods have emerged as the state-of-the-art for accelerating MRI scans, enabling higher spatial and temporal resolutions. However, the high resolution of these scans generates massive data volumes, leading to challenges in transmission, storage, and real-time processing. This is particularly pronounced in functional MRI, where hundreds of volumetric acquisitions further exacerbate these demands. Edge computing with FPGAs presents a promising solution for enabling PD-AI reconstruction near the MRI sensors, reducing data transfer and storage bottlenecks. However, this requires optimization of PD-AI models for hardware efficiency through quantization and bypassing traditional FFT-based approaches, which can be a limitation due to their computational demands. In this work, we propose a novel PD-AI computational MRI approach optimized for FPGA-based edge computing devices, leveraging 8-bit complex data quantization and eliminating redundant FFT/IFFT operations. Our results show that this strategy improves computational efficiency while maintaining reconstruction quality comparable to conventional PD-AI methods, and outperforms standard clinical methods. Our approach presents an opportunity for high-resolution MRI reconstruction on resource-constrained devices, highlighting its potential for real-world deployment.
AI Accelerators for Large Language Model In-ference: Architecture Analysis and Scaling Strategies
Authors: Amit Sharma
Date: 2025-05-13 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Large Language Model

The rapid growth of large-language models (LLMs) is driving a new wave of specialized hardware for inference. This paper presents the first workload-centric, cross-architectural performance study of commercial AI accelerators, spanning GPU-based chips, hybrid packages, and wafer-scale engines. We compare memory hierarchies, compute fabrics, and on-chip interconnects, and observe up to 3.7x performance variation across architectures as batch size and sequence length change. Four scaling techniques for trillion-parameter models are examined; expert parallelism offers an 8.4x parameter-to-compute advantage but incurs 2.1x higher latency variance than tensor parallelism. These findings provide quantitative guidance for matching workloads to accelerators and reveal architectural gaps that next-generation designs must address.
Emerging ML-AI Techniques for Analog and RF EDA
Authors: Zhengfeng Wu, Ziyi Chen, Nnaemeka Achebe, Vaibhav V. Rao, Pratik Shrestha, Ioannis Savidis
Date: 2025-05-12 00:00:00
Tasks: Survey

This survey explores the integration of machine learning (ML) into EDA workflows for analog and RF circuits, addressing challenges unique to analog design, which include complex constraints, nonlinear design spaces, and high computational costs. State-of-the-art learning and optimization techniques are reviewed for circuit tasks such as constraint formulation, topology generation, device modeling, sizing, placement, and routing. The survey highlights the capability of ML to enhance automation, improve design quality, and reduce time-to-market while meeting the target specifications of an analog or RF circuit. Emerging trends and cross-cutting challenges, including robustness to variations and considerations of interconnect parasitics, are also discussed.
Training Cross-Morphology Embodied AI Agents: From Practical Challenges to Theoretical Foundations
Authors: Shaoshan Liu, Fan Wang, Hongjun Zhou, Yuanfeng Wang
Date: 2025-06-04 00:00:00

While theory and practice are often seen as separate domains, this article shows that theoretical insight is essential for overcoming real-world engineering barriers. We begin with a practical challenge: training a cross-morphology embodied AI policy that generalizes across diverse robot morphologies. We formalize this as the Heterogeneous Embodied Agent Training (HEAT) problem and prove it reduces to a structured Partially Observable Markov Decision Process (POMDP) that is PSPACE-complete. This result explains why current reinforcement learning pipelines break down under morphological diversity, due to sequential training constraints, memory-policy coupling, and data incompatibility. We further explore Collective Adaptation, a distributed learning alternative inspired by biological systems. Though NEXP-complete in theory, it offers meaningful scalability and deployment benefits in practice. This work illustrates how computational theory can illuminate system design trade-offs and guide the development of more robust, scalable embodied AI. For practitioners and researchers to explore this problem, the implementation code of this work has been made publicly available at https://github.com/airs-admin/HEAT
Higher-Order Responsibility
Authors: Junli Jiang, Pavel Naumov
Date: 2025-06-01 00:00:00
Tasks: Decision Making, Ethics

In ethics, individual responsibility is often defined through Frankfurt's principle of alternative possibilities. This definition is not adequate in a group decision-making setting because it often results in the lack of a responsible party or "responsibility gap''. One of the existing approaches to address this problem is to consider group responsibility. Another, recently proposed, approach is "higher-order'' responsibility. The paper considers the problem of deciding if higher-order responsibility up to degree $d$ is enough to close the responsibility gap. The main technical result is that this problem is $\Pi_{2d+1}$-complete.
ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding
Authors: Ankit Pal, Jung-Oh Lee, Xiaoman Zhang, Malaikannan Sankarasubbu, Seunghyeon Roh, Won Jung Kim, Meesun Lee, Pranav Rajpurkar
Date: 2025-06-04 00:00:00
Tasks: Negation, Negation Detection, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We present ReXVQA, the largest and most comprehensive benchmark for visual question answering (VQA) in chest radiology, comprising approximately 696,000 questions paired with 160,000 chest X-rays studies across training, validation, and test sets. Unlike prior efforts that rely heavily on template based queries, ReXVQA introduces a diverse and clinically authentic task suite reflecting five core radiological reasoning skills: presence assessment, location analysis, negation detection, differential diagnosis, and geometric reasoning. We evaluate eight state-of-the-art multimodal large language models, including MedGemma-4B-it, Qwen2.5-VL, Janus-Pro-7B, and Eagle2-9B. The best-performing model (MedGemma) achieves 83.24% overall accuracy. To bridge the gap between AI performance and clinical expertise, we conducted a comprehensive human reader study involving 3 radiology residents on 200 randomly sampled cases. Our evaluation demonstrates that MedGemma achieved superior performance (83.84% accuracy) compared to human readers (best radiology resident: 77.27%), representing a significant milestone where AI performance exceeds expert human evaluation on chest X-ray interpretation. The reader study reveals distinct performance patterns between AI models and human experts, with strong inter-reader agreement among radiologists while showing more variable agreement patterns between human readers and AI models. ReXVQA establishes a new standard for evaluating generalist radiological AI systems, offering public leaderboards, fine-grained evaluation splits, structured explanations, and category-level breakdowns. This benchmark lays the foundation for next-generation AI systems capable of mimicking expert-level clinical reasoning beyond narrow pathology classification. Our dataset will be open-sourced at https://huggingface.co/datasets/rajpurkarlab/ReXVQA
Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
Authors: Utkarsh Utkarsh, Pengfei Cai, Alan Edelman, Rafael Gomez-Bombarelli, Christopher Vincent Rackauckas
Date: 2025-06-04 00:00:00

Deep generative models have recently been applied to physical systems governed by partial differential equations (PDEs), offering scalable simulation and uncertainty-aware inference. However, enforcing physical constraints, such as conservation laws (linear and nonlinear) and physical consistencies, remains challenging. Existing methods often rely on soft penalties or architectural biases that fail to guarantee hard constraints. In this work, we propose Physics-Constrained Flow Matching (PCFM), a zero-shot inference framework that enforces arbitrary nonlinear constraints in pretrained flow-based generative models. PCFM continuously guides the sampling process through physics-based corrections applied to intermediate solution states, while remaining aligned with the learned flow and satisfying physical constraints. Empirically, PCFM outperforms both unconstrained and constrained baselines on a range of PDEs, including those with shocks, discontinuities, and sharp features, while ensuring exact constraint satisfaction at the final solution. Our method provides a general framework for enforcing hard constraints in both scientific and general-purpose generative models, especially in applications where constraint satisfaction is essential.
Risk and Reward of Transitioning from a National to a Zonal Electricity Market in Great Britain
Authors: Lukas Franken, Andrew Lyden, Daniel Friedrich
Date: 2025-06-04 00:00:00

More spatially granular electricity wholesale markets promise more efficient operation and better asset siting in highly renewable power systems. Great Britain is considering moving from its current single-price national wholesale market to a zonal design. Existing studies reach varying and difficult-to-reconcile conclusions about the desirability of a zonal market in GB, partly because they rely on models that vary in their transparency and assumptions about future power systems. Using a novel open-source electricity market model, calibrated to match observed network behaviour, this article quantifies consumer savings, unit-level producer surplus impacts, and broader socioeconomic benefits that would have arisen had a six-zone market operated in Great Britain during 2022-2024. In the absence of mitigating policies, it is estimated that during those three years GB consumers would save approximately {\pounds}9.4/MWh (equalling an average of more than {\pounds}2.3B per year), but generators in northern regions would experience revenue reductions of 30-40\%. Policy interventions can restore these units' national market revenues to up to 97\% while still preserving around {\pounds}3.1/MWh in consumer savings (about {\pounds}750M per year). It is further estimated that the current system could achieve approximately {\pounds}380-{\pounds}770 million in annual welfare gain during 2022-2024 through improved operational efficiency alone. The drivers behind these benefits, notably wind curtailment volumes, are expected to become more pronounced towards 2030, suggesting that purely operationally achieved annual benefits of around {\pounds}1-2 billion beyond 2029 are likely. It is found that the scale of these benefits would outweigh the potential downsides related to increases in the cost of capital that have been estimated elsewhere.