UMBC Computer Science and Electrical Engineering Department

Permanent URI for this collectionhttp://hdl.handle.net/11603/50

The Computer Science and Electrical Engineering Department aims to maintain a program of excellence in teaching, research, and service for all of its programs. At the undergraduate level, we will provide students with a firm foundation of both the theory and practice of Computer Science and Computer Engineering. Our curricula also give students the social, ethical, and liberal education needed to make significant contributions to society. Students receiving a bachelor’s degree are ready to enter the work force as productive computer scientists or computer engineers, or to continue their education at the graduate or professional level.

At the graduate level, we are committed to developing the research and professional capabilities of students in Computer Science, Computer Engineering, Electrical Engineering and Cybersecurity. Our programs provide a deeper mastery of the basics of these fields, as well as opportunities to collaborate on leading-edge research with our faculty. Our faculty are engaged in both practical and theoretical research, often in partnership with government agencies, private industry and non-governmental organizations. The aim of this research is to advance knowledge within our disciplines and also to contribute to solving problems faced by our society.

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On Momentum Acceleration for Randomized Coordinate Descent in Matrix Completion
(IEEE, 2025-04) Callahan, Matthew; Vu, Trung; Raich, Raviv
Matrix completion plays an important role in machine learning and signal processing, with applications ranging from recommender systems to image inpainting. Many approaches have been considered to solve the problem and some offer computationally efficient solutions. In particular, a highly-efficient random coordinate descent approach reduces the per-epoch computation dramatically. This paper is concerned with further improvement of computational efficiency to expand the range of problem sizes and conditions that can be solved. Momentum acceleration is a well-known method to improve the efficiency of iterative algorithms, but applying it to random coordinate descent methods without increasing the computational complexity is non-trivial. To address this challenge, we introduce a momentum-accelerated randomized coordinate descent for matrix completion approach that does not increase computational complexity by accelerating at the level of epochs. Additionally, we propose an analysis-driven, tuning-free method for step size selection. To that end, we offer a convergence rate analysis for the algorithm. Using numerical evaluations, we demonstrate the competitiveness of the method and verify the theoretical analysis.
Matrix Factorization for Inferring Associations and Missing Links
(2025-03-06) Barron, Ryan; Eren, Maksim; Truong, Duc P.; Matuszek, Cynthia; Wendelberger, James; Dorn, Mary F.; Alexandrov, Boian
Missing link prediction is a method for network analysis, with applications in recommender systems, biology, social sciences, cybersecurity, information retrieval, and Artificial Intelligence (AI) reasoning in Knowledge Graphs. Missing link prediction identifies unseen but potentially existing connections in a network by analyzing the observed patterns and relationships. In proliferation detection, this supports efforts to identify and characterize attempts by state and non-state actors to acquire nuclear weapons or associated technology - a notoriously challenging but vital mission for global security. Dimensionality reduction techniques like Non-Negative Matrix Factorization (NMF) and Logistic Matrix Factorization (LMF) are effective but require selection of the matrix rank parameter, that is, of the number of hidden features, k, to avoid over/under-fitting. We introduce novel Weighted (WNMFk), Boolean (BNMFk), and Recommender (RNMFk) matrix factorization methods, along with ensemble variants incorporating logistic factorization, for link prediction. Our methods integrate automatic model determination for rank estimation by evaluating stability and accuracy using a modified bootstrap methodology and uncertainty quantification (UQ), assessing prediction reliability under random perturbations. We incorporate Otsu threshold selection and k-means clustering for Boolean matrix factorization, comparing them to coordinate descent-based Boolean thresholding. Our experiments highlight the impact of rank k selection, evaluate model performance under varying test-set sizes, and demonstrate the benefits of UQ for reliable predictions using abstention. We validate our methods on three synthetic datasets (Boolean and uniformly distributed) and benchmark them against LMF and symmetric LMF (symLMF) on five real-world protein-protein interaction networks, showcasing an improved prediction performance.
Introduction to the Special Issue on Large Language Models, Conversational Systems, and Generative AI in Health - Part 1
(ACM, 2025-03-20) Zhou, Jiayu; Gaur, Manas; Rahmani, Amir M.; Chandra Guntuku, Sharath; Jiang, Xiaofan (Fred); Naumann, Tristan
Dialogue systems are designed to offer human users social support or functional services through natural language interactions. Traditional conversation research has put significant emphasis on a system’s response-ability, including its capacity to understand dialogue context and generate appropriate responses. However, the key element of proactive behavior—a crucial aspect of intelligent conversations—is often overlooked in these studies. Proactivity empowers conversational agents to lead conversations towards achieving pre-defined targets or fulfilling specific goals on the system side. Proactive dialogue systems are equipped with advanced techniques to handle complex tasks, requiring strategic and motivational interactions, thus representing a significant step towards artificial general intelligence. Motivated by the necessity and challenges of building proactive dialogue systems, we provide a comprehensive review of various prominent problems and advanced designs for implementing proactivity into different types of dialogue systems, including open-domain dialogues, task-oriented dialogues, and information-seeking dialogues. We also discuss real-world challenges that require further research attention to meet application needs in the future, such as proactivity in dialogue systems that are based on large language models, proactivity in hybrid dialogues, evaluation protocols and ethical considerations for proactive dialogue systems. By providing a quick access and overall picture of the proactive dialogue systems domain, we aim to inspire new research directions and stimulate further advancements towards achieving the next level of conversational AI capabilities, paving the way for more dynamic and intelligent interactions within various application domains.
Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models
(2025-03-08) Khan, Md Azim; Gangopadhyay, Aryya; Wang, Jianwu; Erbacher, Robert F.
Situational awareness applications rely heavily on real-time processing of visual and textual data to provide actionable insights. Vision language models (VLMs) have become essential tools for interpreting complex environments by connecting visual inputs with natural language descriptions. However, these models often face computational challenges, especially when required to perform efficiently in real environments. This research presents a novel vision language model (VLM) framework that leverages frequency domain transformations and low-rank adaptation (LoRA) to enhance feature extraction, scalability, and efficiency. Unlike traditional VLMs, which rely solely on spatial-domain representations, our approach incorporates Discrete Fourier Transform (DFT) based low-rank features while retaining pretrained spatial weights, enabling robust performance in noisy or low visibility scenarios. We evaluated the proposed model on caption generation and Visual Question Answering (VQA) tasks using benchmark datasets with varying levels of Gaussian noise. Quantitative results demonstrate that our model achieves evaluation metrics comparable to state-of-the-art VLMs, such as CLIP ViT-L/14 and SigLIP. Qualitative analysis further reveals that our model provides more detailed and contextually relevant responses, particularly for real-world images captured by a RealSense camera mounted on an Unmanned Ground Vehicle (UGV).
Improvisational Computational Storytelling in Open Worlds
(Springer Nature, 2016-10-22) Martin, Lara J.; Harrison, Brent; Riedl, Mark O.
Improvisational storytelling involves one or more people interacting in real-time to create a story without advanced notice of topic or theme. Human improvisation occurs in an open-world that can be in any state and characters can perform any behaviors expressible through natural language. We propose the grand challenge of computational improvisational storytelling in open-world domains. The goal is to develop an intelligent agent that can sensibly co-create a story with one or more humans through natural language. We lay out some of the research challenges and propose two agent architectures that can provide the basis for exploring the research issues surrounding open-world human-agent interactions.
Impact of increased anthropogenic Amazon wildfires on Antarctic Sea ice melt via albedo reduction
(Cambridge University Press, 2025-03-10) Chakraborty, Sudip; Devnath, Maloy Kumar; Jabeli, Atefeh; Kulkarni, Chhaya; Boteju, Gehan; Wang, Jianwu; Janeja, Vandana
This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat (~ 33,000 km²) during the presence of BC AARs as compared to ~13,000 km² during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions.
GPT's Devastated and LLaMA's Content: Emotion Representation Alignment in LLMs for Keyword-based Generation
(2025-03-14) Choudhury, Shadab Hafiz; Kumar, Asha; Martin, Lara J.
In controlled text generation using large language models (LLMs), gaps arise between the language model's interpretation and human expectations. We look at the problem of controlling emotions in keyword-based sentence generation for both GPT-4 and LLaMA-3. We selected four emotion representations: Words, Valence-Arousal-Dominance (VAD) dimensions expressed in both Lexical and Numeric forms, and Emojis. Our human evaluation looked at the Human-LLM alignment for each representation, as well as the accuracy and realism of the generated sentences. While representations like VAD break emotions into easy-to-compute components, our findings show that people agree more with how LLMs generate when conditioned on English words (e.g., "angry") rather than VAD scales. This difference is especially visible when comparing Numeric VAD to words. However, we found that converting the originally-numeric VAD scales to Lexical scales (e.g., +4.0 becomes "High") dramatically improved agreement. Furthermore, the perception of how much a generated sentence conveys an emotion is highly dependent on the LLM, representation type, and which emotion it is.
From Guessing to Asking: An Approach to Resolving the Persona Knowledge Gap in LLMs during Multi-Turn Conversations
(2025-03-16) Baskar, Sarvesh; Verelakar, Tanmay Tulsidas; Parthasarathy, Srinivasan; Gaur, Manas
In multi-turn dialogues, large language models (LLM) face a critical challenge of ensuring coherence while adapting to user-specific information. This study introduces the persona knowledge gap, the discrepancy between a model's internal understanding and the knowledge required for coherent, personalized conversations. While prior research has recognized these gaps, computational methods for their identification and resolution remain underexplored. We propose Conversation Preference Elicitation and Recommendation (CPER), a novel framework that dynamically detects and resolves persona knowledge gaps using intrinsic uncertainty quantification and feedback-driven refinement. CPER consists of three key modules: a Contextual Understanding Module for preference extraction, a Dynamic Feedback Module for measuring uncertainty and refining persona alignment, and a Persona-Driven Response Generation module for adapting responses based on accumulated user context. We evaluate CPER on two real-world datasets: CCPE-M for preferential movie recommendations and ESConv for mental health support. Using A/B testing, human evaluators preferred CPER's responses 42% more often than baseline models in CCPE-M and 27% more often in ESConv. A qualitative human evaluation confirms that CPER's responses are preferred for maintaining contextual relevance and coherence, particularly in longer (12+ turn) conversations.
Event-Based Crossing Dataset (EBCD)
(2025-03-21) Mule, Joey; Challagundla, Dhandeep; Saini, Rachit; Islam, Riadul
Event-based vision revolutionizes traditional image sensing by capturing asynchronous intensity variations rather than static frames, enabling ultrafast temporal resolution, sparse data encoding, and enhanced motion perception. While this paradigm offers significant advantages, conventional event-based datasets impose a fixed thresholding constraint to determine pixel activations, severely limiting adaptability to real-world environmental fluctuations. Lower thresholds retain finer details but introduce pervasive noise, whereas higher thresholds suppress extraneous activations at the expense of crucial object information. To mitigate these constraints, we introduce the Event-Based Crossing Dataset (EBCD), a comprehensive dataset tailored for pedestrian and vehicle detection in dynamic outdoor environments, incorporating a multi-thresholding framework to refine event representations. By capturing event-based images at ten distinct threshold levels (4, 8, 12, 16, 20, 30, 40, 50, 60, and 75), this dataset facilitates an extensive assessment of object detection performance under varying conditions of sparsity and noise suppression. We benchmark state-of-the-art detection architectures-including YOLOv4, YOLOv7, EfficientDet-b0, MobileNet-v1, and Histogram of Oriented Gradients (HOG)-to experiment upon the nuanced impact of threshold selection on detection performance. By offering a systematic approach to threshold variation, we foresee that EBCD fosters a more adaptive evaluation of event-based object detection, aligning diverse neuromorphic vision with real-world scene dynamics. We present the dataset as publicly available to propel further advancements in low-latency, high-fidelity neuromorphic imaging: this https URL
Enhancing prosthetic hand control: A synergistic multi-channel electroencephalogram
(Cambridge University Press, 2024-11-28) Maibam, Pooya Chanu; Pei, Dingyi; Olikkal, Parthan Sathishkumar; Vinjamuri, Ramana; Kakoty, Nayan M.
Electromyogram (EMG) has been a fundamental approach for prosthetic hand control. However it is limited by the functionality of residual muscles and muscle fatigue. Currently, exploring temporal shifts in brain networks and accurately classifying noninvasive electroencephalogram (EEG) for prosthetic hand control remains challenging. In this manuscript, it is hypothesized that the coordinated and synchronized temporal patterns within the brain network, termed as brain synergy, contain valuable information to decode hand movements. 32-channel EEGs were acquired from 10 healthy participants during hand grasp and open. Synergistic spatial distribution pattern and power spectra of brain activity were investigated using independent component analysis of EEG. Out of 32 EEG channels, 15 channels spanning the frontal, central and parietal regions were strategically selected based on the synergy of spatial distribution pattern and power spectrum of independent components. Time-domain and synergistic features were extracted from the selected 15 EEG channels. These features were employed to train a Bayesian optimizer-based support vector machine (SVM). The optimized SVM classifier could achieve an average testing accuracy of 94.39 ±± \pm .84% using synergistic features. The paired t-test showed that synergistic features yielded significantly higher area under curve values (p < .05) compared to time-domain features in classifying hand movements. The output of the classifier was employed for the control of the prosthetic hand. This synergistic approach for analyzing temporal activities in motor control and control of prosthetic hands have potential contributions to future research. It addresses the limitations of EMG-based approaches and emphasizes the effectiveness of synergy-based control for prostheses
Elvis: A Highly Scalable Virtual Internet Simulator
(Inventive Research Organization, 2025-02-13) Boddu, Dheeraj Kumar
Elvis is a highly scalable virtual Internet simulator that can simulate up to a hundred thousand networked machines communicating over TCP/IP on a single off-the-shelf desktop computer. This research describes the construction of Elvis in Rust, a new memory-safe systems programming language, and the design patterns that enabled us to reach scalability targets. Traffic in the simulation is generated from models based on user behavior research and profiling of large web servers. Additionally, a Network Description Language (NDL) was designed to describe large Internet simulations.
Electroencephalogram based Control of Prosthetic Hand using Optimizable Support Vector Machine
(ACM, 2023-11-02) Pooya Chanu, Maibam; Pei, Dingyi; Olikkal, Parthan Sathishkumar; Vinjamuri, Ramana; Kakoty, Nayan M.
Research on electromyogram (EMG) controlled prosthetic hands has advanced significantly, enriching the social and professional lives of people with hand amputation. Even so, the non-functionality of motor neurons in the remnant muscles impedes the generation of EMG as a control signal. However, such people have the same ability as healthy individuals to generate motor cortical activity. The work presented in this paper investigates electroencephalogram (EEG)-based control of a prosthetic hand. EEG of 10 healthy subjects performing the grasping operations were acquired for classification of hand movements. 15 EEG channels were selected to classify hand open and close operations. Hand movement-class-specific time-domain features were extracted from the filtered EEG. A support vector machine (SVM) was employed with 24-fold cross-validation for classification using extracted features. SVM hyper-parameters for the classification model were optimized with a Bayesian optimizer with a minimum prediction error as an objective function. During training and testing of the classifier model, an average accuracy of 96.8 ± 0.98% and 93.4 ± 1.16% respectively, were achieved across the subjects. The trained classifier model was employed to control prosthetic hand open and close operations. This study demonstrates that EEG can be used to control a prosthetic hand by amputees with motor neuron disabilities.
Descriptor: Smart Event Face Dataset (SEFD)
(IEEE, 2024-10-23) Islam, Riadul; Tummala, Sri Ranga Sai Krishna; Mule, Joey; Kankipati, Rohith; Jalapally, Suraj Kumar; Challagundla, Dhandeep; Howard, Chad; Robucci, Ryan
Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. IEEE SOCIETY/COUNCIL Signal Processing Society (SPS) DATA TYPE/LOCATION Images; MD, USA DATA DOI/PID 10.21227/bw2e-dj78
Deep Image Segmentation for Defect Detection in Photo-lithography Fabrication
(IEEE, 2023-05-24) Paul, Omari; Abrar, Sakib; Mu, Richard; Islam, Riadul; Samad, Manar D.
Surface acoustic wave (SAW) sensors with increasingly unique and refined designed patterns are often developed using the lithographic fabrication processes. Emerging applications of SAW sensors often require novel materials, which may present uncharted fabrication outcomes. The fidelity of the SAW sensor performance is often correlated with the ability to restrict the presence of defects in post-fabrication. Therefore, it is critical to have effective means to detect the presence of defects within the SAW sensor. However, labor-intensive manual labeling is often required due to the need for precision identification and classification of surface features for increased confidence in model accuracy. One approach to automating defect detection is to leverage effective machine learning techniques to analyze and quantify defects within the SAW sensor. In this paper, we propose a machine learning approach using a deep convolutional autoencoder to segment surface features semantically. The proposed deep image autoencoder takes a grayscale input image and generates a color image segmenting the defect region in red, metallic interdigital transducing (IDT) fingers in green, and the substrate region in blue. Experimental results demonstrate promising segmentation scores in locating the defects and regions of interest for a novel SAW sensor variant. The proposed method can automate the process of localizing and measuring post-fabrication defects at the pixel level that may be missed by error-prone visual inspection.
Decoding motor execution and motor imagery from EEG with deep learning and source localization
(Elsevier, 2025-06-01) Kaviri, Sina Makhdoomi; Vinjamuri, Ramana
The use of noninvasive imaging techniques has become pivotal in understanding human brain functionality. While modalities like MEG and fMRI offer excellent spatial resolution, their limited temporal resolution, often measured in seconds, restricts their application in real-time brain activity monitoring. In contrast, EEG provides superior temporal resolution, making it ideal for real-time applications in brain–computer interface systems. In this study, we combined deep learning with source localization to classify two motor task types: motor execution and motor imagery. For motor imagery tasks—left hand, right hand, both feet, and tongue—we transformed EEG signals into cortical activity maps using Minimum Norm Estimation (MNE), dipole fitting, and beamforming. These were analyzed with a custom ResNet CNN, where beamforming achieved the highest accuracy of 99.15%, outperforming most traditional methods. For motor execution involving six types of reach-and-grasp tasks, beamforming achieved 90.83% accuracy compared to 56.39% from a sensor domain approach (ICA + PSD + TSCR-Net). These results underscore the significant advantages of integrating source localization with deep learning for EEG-based motor task classification, demonstrating that source localization techniques greatly enhance classification accuracy compared to sensor domain approaches.
Decoding and generating synergy-based hand movements using electroencephalography during motor execution and motor imagery
(Elsevier, 2025-06-01) Pei, Dingyi; Vinjamuri, Ramana
Brain-machine interfaces (BMIs) have proven valuable in motor control and rehabilitation. Motor imagery (MI) is a key tool for developing BMIs, particularly for individuals with impaired limb function. Motor planning and internal programming are hypothesized to be similar during motor execution (ME) and motor imagination. The anatomical and functional similarity between motor execution and motor imagery suggests that synergy-based movement generation can be achieved by extracting neural correlates of synergies or movement primitives from motor imagery. This study explored the feasibility of synergy-based hand movement generation using electroencephalogram (EEG) from imagined hand movements. Ten subjects participated in an experiment to imagine and execute hand movement tasks while their hand kinematics and neural activity were recorded. Hand kinematic synergies derived from executed movements were correlated with EEG spectral features to create a neural decoding model. This model was used to decode the weights of kinematic synergies from motor imagery EEG. These decoded weights were then combined with kinematic synergies to generate hand movements. As a result, the decoding model successfully predicted hand joint angular velocity patterns associated with grasping different objects. This adaptability demonstrates the model's ability to capture the motor control characteristics of ME and MI, advancing our understanding of MI-based neural decoding. The results hold promise for potential applications in noninvasive synergy-based neuromotor control and rehabilitation for populations with upper limb motor disabilities.
Cooperative and Competitive Functional Connectivity Based on Improved Ising Model
(IEEE, 2025-04) Wei, Gengqian; Liang, Chuang; Adali, Tulay; Jiang, Rongtao; Zhang, Daoqiang; Calhoun, Vince D.; Qi, Shile
As a highly interconnected complex network system, the brain exhibits changes in interactions due to common brain disorders. Studying changes in brain network interactions can help us quantitatively analyze functional network patterns and changes in these patterns that are linked to brain disorders. However, relationships between brain regions estimated by most current approaches use a single connectivity that does not fully reflect multiple interactions. Here, we propose a novel functional connectivity (FC) construction method, which can estimate both cooperative and competitive (C-C) relationships between the same regions of interest (ROIs) through improved Ising model. We redefine the Ising dynamic equation to represent pairwise interactions from single to C-C relationships. Results show that the estimated C-C connectivities are normally distributed, with intra-subjects’ (n=970) similarity being consistently and significantly higher than inter-subjects’ similarity across datasets. C-C FCs between occipital, parietal, temporal cortex and the limbic system of schizophrenia (SZ, n=178) are more competitive, while healthy control (HC, n=219) tends to be more cooperative. Group differences in C-C patterns between SZ and HC show significant differences in frontal, parietal and occipital regions. The proposed C-C approach provide new insights into the brain dysfunction in SZ, which can also be applied to investigate other brain disorders.
Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction
(2025-03-03) Hossain, Emam; Ferdous, Muhammad Hasan; Wang, Jianwu; Subramanian, Aneesh; Gani, Md Osman
Traditional machine learning and deep learning techniques rely on correlation-based learning, often failing to distinguish spurious associations from true causal relationships, which limits robustness, interpretability, and generalizability. To address these challenges, we propose a causality-driven deep learning framework that integrates Multivariate Granger Causality (MVGC) and PCMCI+ causal discovery algorithms with a hybrid deep learning architecture. Using 43 years (1979-2021) of daily and monthly Arctic Sea Ice Extent (SIE) and ocean-atmospheric datasets, our approach identifies causally significant factors, prioritizes features with direct influence, reduces feature overhead, and improves computational efficiency. Experiments demonstrate that integrating causal features enhances the deep learning model's predictive accuracy and interpretability across multiple lead times. Beyond SIE prediction, the proposed framework offers a scalable solution for dynamic, high-dimensional systems, advancing both theoretical understanding and practical applications in predictive modeling.
ArXrCiM: Architectural Exploration of Application-Specific Resonant SRAM Compute-in-Memory
(IEEE, 2024-11-25) Challagundla, Dhandeep; Bezzam, Ignatius; Islam, Riadul
While general-purpose computing follows von Neumann’s architecture, the data movement between memory and processor elements dictates the processor’s performance. The evolving compute-in-memory (CiM) paradigm tackles this issue by facilitating simultaneous processing and storage within static random-access memory (SRAM) elements. Numerous design decisions taken at different levels of hierarchy affect the figures of merit (FoMs) of SRAM, such as power, performance, area, and yield. The absence of a rapid assessment mechanism for the impact of changes at different hierarchy levels on global FoMs poses a challenge to accurately evaluating innovative SRAM designs. This article presents an automation tool designed to optimize the energy and latency of SRAM designs incorporating diverse implementation strategies for executing logic operations within the SRAM. The tool structure allows easy comparison across different array topologies and various design strategies to result in energy-efficient implementations. Our study involves a comprehensive comparison of over 6900+ distinct design implementation strategies for École Polytechnique Fédérale de Lausanne (EPFL) combinational benchmark circuits on the energy-recycling resonant CiM (rCiM) architecture designed using Taiwan Semiconductor Manufacturing Company (TSMC) 28-nm technology. When provided with a combinational circuit, the tool aims to generate an energy-efficient implementation strategy tailored to the specified input memory and latency constraints. The tool reduces 80.9% of energy consumption on average across all benchmarks while using the six-topology implementation compared with the baseline implementation of single-macro topology by considering the parallel processing capability of rCiM cache size ranging from 4 to 192 kB.
Artifact: Defining and Analyzing Smart Device Passive Mode
(HAL, 2025-03-17) Badolato, Christian; Kullman, Kaur; Papadakis, Nikolaos; Bhatt, Manav; Bouloukakis, Georgios; Engel, Don; Yus, Roberto
This artifact paper presents a guide for the Smart Home IoT Passive Mode Analysis tool and dataset to perform network traffic analysis (NTA) on smart home IoT devices in passive mode. The repository includes: 1) scripts and configurations for processing network traffic capture files and extracting the relevant information; 2) output data files for the experiments conducted; and 3) a link to the raw network capture dataset. The dataset contains 12GB of passive mode traffic from 32 devices across 3 testbeds; between 71 and 196 hours of traffic is present for each device.

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