CCD and CMOS Imaging Sensors

Latest papers

• ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers

  Mohd Moin Khan, Naman Srivastava, Pandarasamy Arjunan · 28 September 2026

  We present \textbf{ENAS}, a hardware-aware Neural Architecture Search (NAS) framework that combines a static feasibility check, a cell-based search space supporting standard, depthwise-separable, and bottleneck blocks with optional skip connections, and a three-stage hybrid search strategy (random $…

• Prescriptive SVD-Inspired Attention via Spectral Energy Retention

  Vasileios Arampatzakis, Vasileios Sevetlidis, George Pavlidis · 22 September 2026

  Self-attention is central to modern Transformer architectures, but its dense dot-product formulation makes it difficult to identify which internal directions are structurally important and which can be modified without disrupting the model. SVD-Inspired Attention (SVDA) addresses part of this proble…

• TERMon: Detecting Persistent Behavioral Threats in Edge AI via Hardware-Native Ternary Runtime Monitor

  Arish Sateesan, Edlira Dushku · 21 September 2026

  Edge AI accelerators are increasingly deployed in safety-critical environments, where model outputs may control physical actuators, make access-control decisions, or trigger alarms. In these settings, runtime failures often remain undetected because model corruption, distribution shift, and adversar…

• Channel-Wise and Token-Aware Post-Training Quantization for Visual State Space Duality

  Jonghyeon Lim, Changhoon Yim · 16 September 2026

  State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision through ViM, VMamba, and Visual State Space Duality (VSSD). Yet the low-bit post-training quantization (PTQ) behavior of VSSD remains insufficiently…

• Hardware-Aware Learned Representation Compression for Distributed In-Sensor Vision

  Chengwei Zhou, Abu Masum, Xuming Chen, Mehran Moghadam, Sreetama Sarkar, Arnab Sanyal, Md Abdullah-Al Kaiser, M. Hassan Najafi, Sercan Aygun, Gourav Datta · 15 September 2026

  In-sensor computing reduces the cost of transmitting high-resolution image data by performing early-stage processing near the sensor. However, the logic chip integrated with a CMOS image sensor (CIS) is tightly constrained in compute and memory, limiting conventional deep neural network partitioning…

• Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge

  Luca Crupi, Lorenzo Lamberti, Alessandro Giusti, Daniele Palossi · 14 September 2026

  Traditional TinyML systems for edge devices achieve high accuracy by relying on fixed-depth models that require a constant number of multiply-accumulate (MAC) operations regardless of the input complexity. This approach wastes critical resources in battery-powered Internet-of-Things (IoT) devices an…

• Elastoformer: Enabling Dynamic Adaptivity via Elastic Model Transformation

  Sudaksh Kalra, Dolly Sapra · 10 September 2026

  EdgeAI systems are increasingly employing computer vision applications to enable intelligent, on-device decision-making in real-time. However, these deployments face highly dynamic operational conditions, with fluctuating constraints on latency, power availability, and memory resources. Deep Neural …

• Deep Microcompression: Structured Pruning and Bit-packed Quantization for Microcontrollers

  Opegbemi Matthias Busoye, Tolulope Matthew Busoye, Eghonghon-aye Eigbe · 7 September 2026

  This paper introduces Deep Microcompression (DMC), a hardware-aware pipeline for deep learning inference on bare-metal microcontrollers. DMC integrates structured pruning, quantization-aware training, and fixed-length bit-packing to achieve a 55.8$\times$ weight compression ratio on LeNet-5 (98.77\%…

• Sustainable Edge Vision via Empirically Calibrated DVFS: Eliminating Thermal Throttling on Passively Cooled Hardware

  Aayush Marasini, Zhaoxian Zhou · 7 September 2026

  Passive cooling eliminates the energy overhead and mechanical failure modes of fans, making it attractive for edge deployment, yet sustained Deep Neural Network (DNN) inference on passively cooled edge Systems-on-Chip (SoCs) is bottlenecked by thermal throttling. To address this, we propose an empir…

• STEMPix: A Phase-Transition-Material-Based Pixel Sensor for Resolving Edge-Movement Direction

  Md Rahatul Islam Udoy, Sumeet Kumar Gupta, Deep Jariwala, Ahmedullah Aziz · 7 September 2026

  This paper proposes a spatio-temporal edge-movement direction pixel (STEMPix) for generating compact direction-aware edge movement information inside a CMOS-compatible image sensor array. The proposed design targets specialized sensing applications where local boundary movement is more important tha…

• Position Matters: Feature Inversion Attacks in ViT Split Inference with Token Reduction and Shuffling

  Stefano Leggio, Giulio Rossolini, Alessandro Biondi · 2 September 2026

  Vision Transformers (ViTs) are increasingly used in split-inference systems, where edge devices transmit intermediate token representations to a remote cloud. In this setting, token reduction lowers computation and communication costs, while token shuffling disrupts the spatial organization of the t…

• TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge

  Kurt M Wilson, Mohaiminul Al Nahian, Abeer Matar A. Almalky, Sadat Shahriyar, Souvik Kundu, Zhishan Guo, Abdullah Al Arafat, Adnan Siraj Rakin · 25 August 2026

  Despite their remarkable success, machine learning models, particularly in vision applications, are alarmingly vulnerable to a range of security threats. One key factor in the attack landscape is the distinction between white-box and black-box threat models, as the latter poses challenges that limit…

• Power-Performance Characterization of TinyML Systems

  Yujie Zhang, Dhananjaya Wijerathne, Zhaoying Li, Tulika Mitra · 25 August 2026

  TinyML systems are enabling machine learning (ML) inference at the edge. However, there is little quantitative analysis of such systems. This paper presents a systematic performance and power characterization of diverse TinyML applications on microcontrollers (MCUs), spanning neural network models, …

• DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation

  Reyhaneh Hosseinzadeh, Parham Zilouchian Moghaddam, Mehdi Modarressi · 4 August 2026

  The emergence of transformer-based deep learning models has brought unprecedented performance across various domains, particularly in natural language processing and computer vision. However, deploying these models, especially on resource-constrained devices, poses significant challenges due to thei…

• The Lift Spectrum: How Measurement-to-Space Adaptivity Shapes Robustness in Image-Free Single-Pixel Sensing

  Yuyuan Han, Jingwei Li, Xiaoxia Zhang, Long Qiu, Chong Wang, Wenxuan Hao, Jiangyu Han, Xinyu Yao, Yuchen He, Hui Chen, Jianbin Liu, Huaibin Zheng · 27 July 2026

  Single-pixel sensing encodes a scene as a short sequence of coded measurements, and image-free methods infer the task directly from that sequence. We show that removing image reconstruction relocates the central design problem to the lift: how 1D measurements become a 2D task representation. We orga…

• On Hardware-Aware Design and Optimization of Edge Intelligence

  Shuo Huai, Hao Kong, Xiangzhong Luo, Di Liu, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu · 21 July 2026

  Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications. However, the complexity of deep learning models and heterogeneity of edge devices make the design of edge intelligence systems a challenging task. Hardware-agn…

• Machine Learning-Based Reconstruction for Resistive Silicon Sensors

  Alexander Aoki, Gaetano Barone, Leena Diehl, Gabriele Giacomini, Vagelis Gkougkousis, Hanshal Goyal, Rohan Kher, Daniel Li, Anna Macchiolo, Yevhenii Padnuik, Daria Senina, Samantha Sunnarborg, Jessica Tang, Alessandro Tricoli, Lixing Wang, Don C. Wong · 14 July 2026

  Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking. In AC-LGADs, the AC pad is coupled to the resistive n$^{+}$ layer through a dielectric layer, while the gain layer remains unsegmented.…

• Quick ViTs: Speeding up Vision Transformers through Equivariance

  David Nordstr\"om, Johan Edstedt, Fredrik Kahl, Georg B\"okman · 7 July 2026

  Natural images exhibit strong geometric regularities: local structures, such as edges, corners, and textures, appear in many orientations and mirror configurations. Since Vision Transformers (ViTs) operate on square image patches, these transformations naturally correspond to the dihedral symmetry g…

• FlexViT: A Flexible FPGA-based Accelerator for Edge Vision Transformers

  Hubert Dymarkowski, Xingjian Fu, Rappy Saha, Jude Haris, Jos\'e Cano · 1 July 2026

  Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heterogeneity of modern hybrid ViT models, which incorporate both fully connected and convolutional layers. This heterogeneity leads to significant variation …

• FrequencyFormer: A Co-Designed Sensor-to-Processor Pipeline for Frequency-Domain Vision Transformer Inference

  Chengwei Zhou, Ovishake Sen, Xuming Chen, Rishith Paramasivam, Shaahin Angizi, Swarup Bhunia, Baibhab Chatterjee, Gourav Datta · 19 June 2026

  Deploying vision transformers (ViTs) on sensor-edge systems is limited not only by on-device compute, but also by the energy and bandwidth required to transmit high-dimensional image data from the sensor to the processor. While in-sensor and near-sensor computing reduce this cost through early featu…

• Running hardware-aware neural architecture search on embedded devices under 512MB of RAM

  Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo, Antonio Frisoli · 16 June 2026

  This document proposes a novel approach to hardware-aware neural architecture search (HW NAS) that considers the resources available on the computing platform running it, enabling its execution on various embedded devices. The presented HW NAS produces tiny convolutional neural networks (CNNs) targe…

• Double-Helix Vision (DH-V2): A Geometry-Based Visual Sampler for Bandwidth-Constrained Perception

  Jinwen Wen · 16 June 2026

  We present Double-Helix Vision (DH), a geometry-based visual sampler that compresses 2D images into compact 1D signals using paired golden-ratio-inspired spiral trajectories. Rather than processing every pixel uniformly, DH employs two phase-shifted helices (Alpha and Beta, offset by 180 degrees) to…

• FPGA-Based Neural Network Accelerators for Space Applications: A Survey

  Pedro Antunes, Artur Podobas · 15 June 2026

  Space missions are becoming increasingly ambitious, necessitating high-performance onboard spacecraft computing systems. In response, field-programmable gate arrays (FPGAs) have garnered significant interest due to their flexibility, cost-effectiveness, and radiation tolerance potential. Concurrentl…

• The Need for Neural ISP in the Small-Pixel Era: How Shrinking Pixels Push Optics to the Limit and Neural Restoration Pushes Back

  Jingxi Li, Neerja Aggarwal, Laurent Gudemann, Shivansh Rao, Vishal Vinod, Tom E. Bishop, Ziv Attar · 9 June 2026

  Smartphone telephoto cameras are approaching a "telephoto physics wall": as pixel pitches shrink toward sub-0.5 micron, the optics remain limited by geometric aberrations, leading to diminishing returns on resolution. Traditional Image Signal Processors (ISPs) cannot eliminate these aberrations, bec…

• Policy-based Foveated Imaging and Perception

  Howard Xiao, Jan Ackermann, Boyang Deng, Gordon Wetzstein · 2 June 2026

  Ultra-high-resolution image sensors offer the potential to capture fine spatial details critical for many visual perception tasks, but acquiring and processing all pixels at full resolution is often infeasible under realistic bandwidth, latency, and power constraints. Existing approaches address thi…